Overview

Brought to you by YData

Dataset statistics

Number of variables100
Number of observations50
Missing cells1415
Missing cells (%)28.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.2 KiB
Average record size in memory802.6 B

Variable types

Categorical51
Numeric31
Text11
Unsupported7

Alerts

GameID has constant value "2016121101"Constant
PlayAttempted has constant value "1"Constant
ExPointResult has constant value "Made"Constant
Safety has constant value "0"Constant
Onsidekick has constant value "0"Constant
PuntResult has constant value "Clean"Constant
InterceptionThrown has constant value "0"Constant
ReturnResult has constant value "Touchback"Constant
FieldGoalResult has constant value "Good"Constant
Fumble has constant value "0"Constant
Sack has constant value "0"Constant
Challenge.Replay has constant value "0"Constant
HomeTeam has constant value "CAR"Constant
AwayTeam has constant value "SD"Constant
Timeout_Team has constant value "SD"Constant
TwoPoint_Prob has constant value "0.0"Constant
Season has constant value "2016"Constant
AbsScoreDiff is highly overall correlated with DefTeamScore and 15 other fieldsHigh correlation
Accepted.Penalty is highly overall correlated with FieldGoalDistance and 13 other fieldsHigh correlation
AirYards is highly overall correlated with FieldGoalDistance and 9 other fieldsHigh correlation
AwayTimeouts_Remaining_Post is highly overall correlated with AwayTimeouts_Remaining_Pre and 10 other fieldsHigh correlation
AwayTimeouts_Remaining_Pre is highly overall correlated with AwayTimeouts_Remaining_Post and 10 other fieldsHigh correlation
Away_WP_post is highly overall correlated with Away_WP_pre and 11 other fieldsHigh correlation
Away_WP_pre is highly overall correlated with Away_WP_post and 7 other fieldsHigh correlation
DefTeamScore is highly overall correlated with AbsScoreDiff and 18 other fieldsHigh correlation
DefensiveTeam is highly overall correlated with AbsScoreDiff and 16 other fieldsHigh correlation
Drive is highly overall correlated with AbsScoreDiff and 24 other fieldsHigh correlation
EPA is highly overall correlated with FieldGoalDistance and 11 other fieldsHigh correlation
ExPoint_Prob is highly overall correlated with AbsScoreDiff and 19 other fieldsHigh correlation
ExpPts is highly overall correlated with FieldGoalDistance and 6 other fieldsHigh correlation
FieldGoalDistance is highly overall correlated with AbsScoreDiff and 52 other fieldsHigh correlation
Field_Goal_Prob is highly overall correlated with ExpPts and 6 other fieldsHigh correlation
FirstDown is highly overall correlated with EPA and 7 other fieldsHigh correlation
GoalToGo is highly overall correlated with AirYards and 16 other fieldsHigh correlation
HomeTimeouts_Remaining_Post is highly overall correlated with AbsScoreDiff and 13 other fieldsHigh correlation
HomeTimeouts_Remaining_Pre is highly overall correlated with AbsScoreDiff and 13 other fieldsHigh correlation
Home_WP_post is highly overall correlated with Away_WP_post and 11 other fieldsHigh correlation
Home_WP_pre is highly overall correlated with Away_WP_post and 7 other fieldsHigh correlation
No_Score_Prob is highly overall correlated with AbsScoreDiff and 7 other fieldsHigh correlation
Opp_Field_Goal_Prob is highly overall correlated with Drive and 9 other fieldsHigh correlation
Opp_Safety_Prob is highly overall correlated with ExpPts and 7 other fieldsHigh correlation
Opp_Touchdown_Prob is highly overall correlated with Drive and 6 other fieldsHigh correlation
PassAttempt is highly overall correlated with AirYards and 20 other fieldsHigh correlation
PassLength is highly overall correlated with AirYards and 5 other fieldsHigh correlation
PassLocation is highly overall correlated with ExPoint_Prob and 4 other fieldsHigh correlation
PassOutcome is highly overall correlated with EPA and 9 other fieldsHigh correlation
Passer is highly overall correlated with AbsScoreDiff and 14 other fieldsHigh correlation
Passer_ID is highly overall correlated with AbsScoreDiff and 19 other fieldsHigh correlation
Penalty.Yards is highly overall correlated with Accepted.Penalty and 13 other fieldsHigh correlation
PlayTimeDiff is highly overall correlated with FieldGoalDistanceHigh correlation
PlayType is highly overall correlated with Accepted.Penalty and 20 other fieldsHigh correlation
PosTeamScore is highly overall correlated with AbsScoreDiff and 18 other fieldsHigh correlation
QBHit is highly overall correlated with FieldGoalDistance and 6 other fieldsHigh correlation
Receiver is highly overall correlated with Accepted.Penalty and 17 other fieldsHigh correlation
Receiver_ID is highly overall correlated with Accepted.Penalty and 18 other fieldsHigh correlation
Reception is highly overall correlated with EPA and 13 other fieldsHigh correlation
RunGap is highly overall correlated with Accepted.Penalty and 15 other fieldsHigh correlation
RunLocation is highly overall correlated with Accepted.Penalty and 13 other fieldsHigh correlation
RushAttempt is highly overall correlated with FieldGoalDistance and 17 other fieldsHigh correlation
Rusher is highly overall correlated with Accepted.Penalty and 13 other fieldsHigh correlation
Rusher_ID is highly overall correlated with Accepted.Penalty and 18 other fieldsHigh correlation
Safety_Prob is highly overall correlated with Drive and 10 other fieldsHigh correlation
ScoreDiff is highly overall correlated with AbsScoreDiff and 18 other fieldsHigh correlation
SideofField is highly overall correlated with Away_WP_post and 6 other fieldsHigh correlation
TimeSecs is highly overall correlated with AwayTimeouts_Remaining_Post and 17 other fieldsHigh correlation
TimeUnder is highly overall correlated with AwayTimeouts_Remaining_Post and 13 other fieldsHigh correlation
Timeout_Indicator is highly overall correlated with AbsScoreDiff and 27 other fieldsHigh correlation
Touchdown is highly overall correlated with Away_WP_post and 9 other fieldsHigh correlation
Touchdown_Prob is highly overall correlated with ExpPts and 4 other fieldsHigh correlation
WPA is highly overall correlated with EPA and 10 other fieldsHigh correlation
Win_Prob is highly overall correlated with AbsScoreDiff and 10 other fieldsHigh correlation
Yards.Gained is highly overall correlated with EPA and 9 other fieldsHigh correlation
YardsAfterCatch is highly overall correlated with EPA and 14 other fieldsHigh correlation
airEPA is highly overall correlated with Accepted.Penalty and 12 other fieldsHigh correlation
airWPA is highly overall correlated with Accepted.Penalty and 14 other fieldsHigh correlation
down is highly overall correlated with ExPoint_Prob and 4 other fieldsHigh correlation
posteam is highly overall correlated with AbsScoreDiff and 16 other fieldsHigh correlation
posteam_timeouts_pre is highly overall correlated with AwayTimeouts_Remaining_Post and 14 other fieldsHigh correlation
qtr is highly overall correlated with AbsScoreDiff and 13 other fieldsHigh correlation
sp is highly overall correlated with EPA and 8 other fieldsHigh correlation
yacEPA is highly overall correlated with Accepted.Penalty and 20 other fieldsHigh correlation
yacWPA is highly overall correlated with Accepted.Penalty and 20 other fieldsHigh correlation
ydsnet is highly overall correlated with DefTeamScore and 13 other fieldsHigh correlation
ydstogo is highly overall correlated with FieldGoalDistanceHigh correlation
yrdline100 is highly overall correlated with DefensiveTeam and 12 other fieldsHigh correlation
yrdln is highly overall correlated with FieldGoalDistance and 9 other fieldsHigh correlation
sp is highly imbalanced (53.1%)Imbalance
Touchdown is highly imbalanced (75.8%)Imbalance
QBHit is highly imbalanced (75.8%)Imbalance
Accepted.Penalty is highly imbalanced (59.8%)Imbalance
Penalty.Yards is highly imbalanced (70.5%)Imbalance
Timeout_Indicator is highly imbalanced (85.9%)Imbalance
ExPoint_Prob is highly imbalanced (85.9%)Imbalance
down has 8 (16.0%) missing valuesMissing
FirstDown has 5 (10.0%) missing valuesMissing
posteam has 3 (6.0%) missing valuesMissing
DefensiveTeam has 3 (6.0%) missing valuesMissing
ExPointResult has 49 (98.0%) missing valuesMissing
TwoPointConv has 50 (100.0%) missing valuesMissing
DefTwoPoint has 50 (100.0%) missing valuesMissing
PuntResult has 49 (98.0%) missing valuesMissing
Passer has 29 (58.0%) missing valuesMissing
Passer_ID has 30 (60.0%) missing valuesMissing
PassOutcome has 29 (58.0%) missing valuesMissing
PassLength has 29 (58.0%) missing valuesMissing
PassLocation has 29 (58.0%) missing valuesMissing
Interceptor has 50 (100.0%) missing valuesMissing
Rusher has 35 (70.0%) missing valuesMissing
Rusher_ID has 35 (70.0%) missing valuesMissing
RunLocation has 35 (70.0%) missing valuesMissing
RunGap has 39 (78.0%) missing valuesMissing
Receiver has 31 (62.0%) missing valuesMissing
Receiver_ID has 32 (64.0%) missing valuesMissing
ReturnResult has 47 (94.0%) missing valuesMissing
Returner has 48 (96.0%) missing valuesMissing
BlockingPlayer has 50 (100.0%) missing valuesMissing
Tackler1 has 24 (48.0%) missing valuesMissing
Tackler2 has 44 (88.0%) missing valuesMissing
FieldGoalResult has 48 (96.0%) missing valuesMissing
FieldGoalDistance has 48 (96.0%) missing valuesMissing
RecFumbTeam has 50 (100.0%) missing valuesMissing
RecFumbPlayer has 50 (100.0%) missing valuesMissing
ChalReplayResult has 50 (100.0%) missing valuesMissing
PenalizedTeam has 46 (92.0%) missing valuesMissing
PenaltyType has 47 (94.0%) missing valuesMissing
PenalizedPlayer has 46 (92.0%) missing valuesMissing
PosTeamScore has 3 (6.0%) missing valuesMissing
DefTeamScore has 3 (6.0%) missing valuesMissing
ScoreDiff has 3 (6.0%) missing valuesMissing
AbsScoreDiff has 3 (6.0%) missing valuesMissing
Timeout_Team has 49 (98.0%) missing valuesMissing
airEPA has 30 (60.0%) missing valuesMissing
yacEPA has 30 (60.0%) missing valuesMissing
Home_WP_pre has 3 (6.0%) missing valuesMissing
Away_WP_pre has 3 (6.0%) missing valuesMissing
Home_WP_post has 4 (8.0%) missing valuesMissing
Away_WP_post has 4 (8.0%) missing valuesMissing
WPA has 2 (4.0%) missing valuesMissing
airWPA has 30 (60.0%) missing valuesMissing
yacWPA has 30 (60.0%) missing valuesMissing
FieldGoalDistance is uniformly distributedUniform
AwayTimeouts_Remaining_Pre is uniformly distributedUniform
TwoPointConv is an unsupported type, check if it needs cleaning or further analysisUnsupported
DefTwoPoint is an unsupported type, check if it needs cleaning or further analysisUnsupported
Interceptor is an unsupported type, check if it needs cleaning or further analysisUnsupported
BlockingPlayer is an unsupported type, check if it needs cleaning or further analysisUnsupported
RecFumbTeam is an unsupported type, check if it needs cleaning or further analysisUnsupported
RecFumbPlayer is an unsupported type, check if it needs cleaning or further analysisUnsupported
ChalReplayResult is an unsupported type, check if it needs cleaning or further analysisUnsupported
TimeUnder has 1 (2.0%) zerosZeros
PlayTimeDiff has 10 (20.0%) zerosZeros
ydstogo has 8 (16.0%) zerosZeros
ydsnet has 5 (10.0%) zerosZeros
Yards.Gained has 22 (44.0%) zerosZeros
AirYards has 31 (62.0%) zerosZeros
YardsAfterCatch has 40 (80.0%) zerosZeros
PosTeamScore has 4 (8.0%) zerosZeros
No_Score_Prob has 4 (8.0%) zerosZeros
Opp_Field_Goal_Prob has 4 (8.0%) zerosZeros
Opp_Safety_Prob has 4 (8.0%) zerosZeros
Opp_Touchdown_Prob has 4 (8.0%) zerosZeros
Field_Goal_Prob has 4 (8.0%) zerosZeros
Safety_Prob has 4 (8.0%) zerosZeros
Touchdown_Prob has 4 (8.0%) zerosZeros
ExpPts has 3 (6.0%) zerosZeros
EPA has 5 (10.0%) zerosZeros
yacEPA has 2 (4.0%) zerosZeros
Win_Prob has 3 (6.0%) zerosZeros
WPA has 5 (10.0%) zerosZeros
yacWPA has 2 (4.0%) zerosZeros

Reproduction

Analysis started2024-09-06 10:58:37.443209
Analysis finished2024-09-06 11:00:34.500894
Duration1 minute and 57.06 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

GameID
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2016121101
50 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016121101
2nd row2016121101
3rd row2016121101
4th row2016121101
5th row2016121101

Common Values

ValueCountFrequency (%)
2016121101 50
100.0%

Length

2024-09-06T14:00:34.738940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:34.850250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2016121101 50
100.0%

Most occurring characters

ValueCountFrequency (%)
1 250
50.0%
2 100
 
20.0%
0 100
 
20.0%
6 50
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 250
50.0%
2 100
 
20.0%
0 100
 
20.0%
6 50
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 250
50.0%
2 100
 
20.0%
0 100
 
20.0%
6 50
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 250
50.0%
2 100
 
20.0%
0 100
 
20.0%
6 50
 
10.0%

Drive
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.56
Minimum13
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:34.947605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile13
Q115
median16
Q316
95-th percentile18
Maximum18
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4165206
Coefficient of variation (CV)0.091036028
Kurtosis-0.40698176
Mean15.56
Median Absolute Deviation (MAD)1
Skewness-0.19415202
Sum778
Variance2.0065306
MonotonicityIncreasing
2024-09-06T14:00:35.077813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
16 17
34.0%
15 12
24.0%
13 6
 
12.0%
17 6
 
12.0%
18 5
 
10.0%
14 4
 
8.0%
ValueCountFrequency (%)
13 6
 
12.0%
14 4
 
8.0%
15 12
24.0%
16 17
34.0%
17 6
 
12.0%
18 5
 
10.0%
ValueCountFrequency (%)
18 5
 
10.0%
17 6
 
12.0%
16 17
34.0%
15 12
24.0%
14 4
 
8.0%
13 6
 
12.0%

qtr
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
3
40 
2
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Length

2024-09-06T14:00:35.213648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:35.320976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring characters

ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

down
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)9.5%
Missing8
Missing (%)16.0%
Memory size532.0 B
1.0
16 
2.0
14 
3.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 16
32.0%
2.0 14
28.0%
3.0 9
18.0%
4.0 3
 
6.0%
(Missing) 8
16.0%

Length

2024-09-06T14:00:35.437273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:35.561555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 16
38.1%
2.0 14
33.3%
3.0 9
21.4%
4.0 3
 
7.1%

Most occurring characters

ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 16
 
12.7%
2 14
 
11.1%
3 9
 
7.1%
4 3
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 16
 
12.7%
2 14
 
11.1%
3 9
 
7.1%
4 3
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 16
 
12.7%
2 14
 
11.1%
3 9
 
7.1%
4 3
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 16
 
12.7%
2 14
 
11.1%
3 9
 
7.1%
4 3
 
2.4%

TimeUnder
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.98
Minimum0
Maximum15
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:35.689853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.25
median5
Q312
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation4.7573359
Coefficient of variation (CV)0.68156675
Kurtosis-1.3543166
Mean6.98
Median Absolute Deviation (MAD)3
Skewness0.31976631
Sum349
Variance22.632245
MonotonicityNot monotonic
2024-09-06T14:00:35.821689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 9
18.0%
5 8
16.0%
12 7
14.0%
15 4
8.0%
3 4
8.0%
1 3
 
6.0%
14 2
 
4.0%
11 2
 
4.0%
10 2
 
4.0%
8 2
 
4.0%
Other values (6) 7
14.0%
ValueCountFrequency (%)
0 1
 
2.0%
1 3
 
6.0%
2 9
18.0%
3 4
8.0%
4 1
 
2.0%
5 8
16.0%
6 1
 
2.0%
7 2
 
4.0%
8 2
 
4.0%
9 1
 
2.0%
ValueCountFrequency (%)
15 4
8.0%
14 2
 
4.0%
13 1
 
2.0%
12 7
14.0%
11 2
 
4.0%
10 2
 
4.0%
9 1
 
2.0%
8 2
 
4.0%
7 2
 
4.0%
6 1
 
2.0%

TimeSecs
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1473.52
Minimum938
Maximum1920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:35.972893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum938
5-th percentile1014.7
Q11180.75
median1534.5
Q31787
95-th percentile1895.15
Maximum1920
Range982
Interquartile range (IQR)606.25

Descriptive statistics

Standard deviation309.15231
Coefficient of variation (CV)0.20980531
Kurtosis-1.4058019
Mean1473.52
Median Absolute Deviation (MAD)295.5
Skewness-0.10254679
Sum73676
Variance95575.153
MonotonicityDecreasing
2024-09-06T14:00:36.130695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1186 3
 
6.0%
1800 3
 
6.0%
1597 3
 
6.0%
1920 2
 
4.0%
1166 2
 
4.0%
1863 2
 
4.0%
1589 2
 
4.0%
1315 1
 
2.0%
1276 1
 
2.0%
1234 1
 
2.0%
Other values (30) 30
60.0%
ValueCountFrequency (%)
938 1
2.0%
978 1
2.0%
1003 1
2.0%
1029 1
2.0%
1058 1
2.0%
1068 1
2.0%
1078 1
2.0%
1121 1
2.0%
1160 1
2.0%
1166 2
4.0%
ValueCountFrequency (%)
1920 2
4.0%
1910 1
 
2.0%
1877 1
 
2.0%
1868 1
 
2.0%
1863 2
4.0%
1858 1
 
2.0%
1825 1
 
2.0%
1800 3
6.0%
1796 1
 
2.0%
1760 1
 
2.0%

PlayTimeDiff
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.74
Minimum0
Maximum51
Zeros10
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:36.272897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median10
Q337.5
95-th percentile45.75
Maximum51
Range51
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation17.517117
Coefficient of variation (CV)0.88739194
Kurtosis-1.605546
Mean19.74
Median Absolute Deviation (MAD)10
Skewness0.26391479
Sum987
Variance306.84939
MonotonicityNot monotonic
2024-09-06T14:00:36.415736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 10
20.0%
4 4
 
8.0%
5 3
 
6.0%
10 3
 
6.0%
6 3
 
6.0%
48 2
 
4.0%
39 2
 
4.0%
43 2
 
4.0%
26 2
 
4.0%
38 2
 
4.0%
Other values (13) 17
34.0%
ValueCountFrequency (%)
0 10
20.0%
4 4
 
8.0%
5 3
 
6.0%
6 3
 
6.0%
7 1
 
2.0%
9 2
 
4.0%
10 3
 
6.0%
25 2
 
4.0%
26 2
 
4.0%
29 1
 
2.0%
ValueCountFrequency (%)
51 1
2.0%
48 2
4.0%
43 2
4.0%
42 1
2.0%
41 2
4.0%
40 1
2.0%
39 2
4.0%
38 2
4.0%
36 1
2.0%
35 1
2.0%

SideofField
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
CAR
29 
SD
21 

Length

Max length3
Median length3
Mean length2.58
Min length2

Characters and Unicode

Total characters129
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSD
2nd rowSD
3rd rowCAR
4th rowCAR
5th rowCAR

Common Values

ValueCountFrequency (%)
CAR 29
58.0%
SD 21
42.0%

Length

2024-09-06T14:00:36.561957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:36.675267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
car 29
58.0%
sd 21
42.0%

Most occurring characters

ValueCountFrequency (%)
C 29
22.5%
A 29
22.5%
R 29
22.5%
S 21
16.3%
D 21
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 29
22.5%
A 29
22.5%
R 29
22.5%
S 21
16.3%
D 21
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 29
22.5%
A 29
22.5%
R 29
22.5%
S 21
16.3%
D 21
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 29
22.5%
A 29
22.5%
R 29
22.5%
S 21
16.3%
D 21
16.3%

yrdln
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.12
Minimum5
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:36.793595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q114
median23
Q335
95-th percentile46.55
Maximum49
Range44
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.282857
Coefficient of variation (CV)0.55069887
Kurtosis-1.0255439
Mean24.12
Median Absolute Deviation (MAD)12
Skewness0.30360499
Sum1206
Variance176.43429
MonotonicityNot monotonic
2024-09-06T14:00:36.934410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
25 6
 
12.0%
9 4
 
8.0%
35 4
 
8.0%
5 4
 
8.0%
14 3
 
6.0%
22 3
 
6.0%
44 2
 
4.0%
17 2
 
4.0%
15 2
 
4.0%
20 2
 
4.0%
Other values (14) 18
36.0%
ValueCountFrequency (%)
5 4
8.0%
7 1
 
2.0%
9 4
8.0%
10 1
 
2.0%
11 2
4.0%
14 3
6.0%
15 2
4.0%
17 2
4.0%
19 1
 
2.0%
20 2
4.0%
ValueCountFrequency (%)
49 1
 
2.0%
48 1
 
2.0%
47 1
 
2.0%
46 2
4.0%
44 2
4.0%
39 1
 
2.0%
38 1
 
2.0%
37 1
 
2.0%
35 4
8.0%
34 2
4.0%

yrdline100
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.52
Minimum5
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:37.070657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q114
median35
Q365.25
95-th percentile81.65
Maximum85
Range80
Interquartile range (IQR)51.25

Descriptive statistics

Standard deviation26.933205
Coefficient of variation (CV)0.69920055
Kurtosis-1.3239532
Mean38.52
Median Absolute Deviation (MAD)24
Skewness0.35481352
Sum1926
Variance725.39755
MonotonicityNot monotonic
2024-09-06T14:00:37.220468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
75 5
 
10.0%
5 4
 
8.0%
9 4
 
8.0%
35 4
 
8.0%
14 3
 
6.0%
22 3
 
6.0%
44 2
 
4.0%
66 2
 
4.0%
83 2
 
4.0%
80 2
 
4.0%
Other values (18) 19
38.0%
ValueCountFrequency (%)
5 4
8.0%
7 1
 
2.0%
9 4
8.0%
10 1
 
2.0%
11 2
4.0%
14 3
6.0%
15 1
 
2.0%
19 1
 
2.0%
22 3
6.0%
24 1
 
2.0%
ValueCountFrequency (%)
85 1
 
2.0%
83 2
 
4.0%
80 2
 
4.0%
75 5
10.0%
70 1
 
2.0%
66 2
 
4.0%
63 1
 
2.0%
54 1
 
2.0%
53 1
 
2.0%
52 1
 
2.0%

ydstogo
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.76
Minimum0
Maximum20
Zeros8
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:37.358705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median9
Q310
95-th percentile16.65
Maximum20
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.0812179
Coefficient of variation (CV)0.65479612
Kurtosis-0.22361916
Mean7.76
Median Absolute Deviation (MAD)2.5
Skewness0.042710802
Sum388
Variance25.818776
MonotonicityNot monotonic
2024-09-06T14:00:37.496990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 16
32.0%
0 8
16.0%
5 6
 
12.0%
9 4
 
8.0%
7 3
 
6.0%
1 3
 
6.0%
13 3
 
6.0%
8 2
 
4.0%
18 2
 
4.0%
12 1
 
2.0%
Other values (2) 2
 
4.0%
ValueCountFrequency (%)
0 8
16.0%
1 3
 
6.0%
5 6
 
12.0%
7 3
 
6.0%
8 2
 
4.0%
9 4
 
8.0%
10 16
32.0%
12 1
 
2.0%
13 3
 
6.0%
15 1
 
2.0%
ValueCountFrequency (%)
20 1
 
2.0%
18 2
 
4.0%
15 1
 
2.0%
13 3
 
6.0%
12 1
 
2.0%
10 16
32.0%
9 4
 
8.0%
8 2
 
4.0%
7 3
 
6.0%
5 6
 
12.0%

ydsnet
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.78
Minimum-10
Maximum75
Zeros5
Zeros (%)10.0%
Negative5
Negative (%)10.0%
Memory size532.0 B
2024-09-06T14:00:37.641737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-8
Q15
median44.5
Q366
95-th percentile70
Maximum75
Range85
Interquartile range (IQR)61

Descriptive statistics

Standard deviation29.549882
Coefficient of variation (CV)0.80342256
Kurtosis-1.594622
Mean36.78
Median Absolute Deviation (MAD)23.5
Skewness-0.23853
Sum1839
Variance873.19551
MonotonicityNot monotonic
2024-09-06T14:00:37.774988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
66 6
 
12.0%
0 5
 
10.0%
70 5
 
10.0%
5 4
 
8.0%
61 4
 
8.0%
-8 3
 
6.0%
22 2
 
4.0%
53 2
 
4.0%
75 2
 
4.0%
44 1
 
2.0%
Other values (16) 16
32.0%
ValueCountFrequency (%)
-10 1
 
2.0%
-8 3
6.0%
-5 1
 
2.0%
0 5
10.0%
5 4
8.0%
9 1
 
2.0%
12 1
 
2.0%
20 1
 
2.0%
21 1
 
2.0%
22 2
 
4.0%
ValueCountFrequency (%)
75 2
 
4.0%
70 5
10.0%
68 1
 
2.0%
66 6
12.0%
65 1
 
2.0%
64 1
 
2.0%
61 4
8.0%
53 2
 
4.0%
51 1
 
2.0%
47 1
 
2.0%

GoalToGo
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0.0
41 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters150
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 41
82.0%
1.0 9
 
18.0%

Length

2024-09-06T14:00:37.909864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:38.018149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 41
82.0%
1.0 9
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 91
60.7%
. 50
33.3%
1 9
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 91
60.7%
. 50
33.3%
1 9
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 91
60.7%
. 50
33.3%
1 9
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 91
60.7%
. 50
33.3%
1 9
 
6.0%

FirstDown
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)4.4%
Missing5
Missing (%)10.0%
Memory size532.0 B
0.0
34 
1.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters135
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 34
68.0%
1.0 11
 
22.0%
(Missing) 5
 
10.0%

Length

2024-09-06T14:00:38.134450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:38.251745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 34
75.6%
1.0 11
 
24.4%

Most occurring characters

ValueCountFrequency (%)
0 79
58.5%
. 45
33.3%
1 11
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 79
58.5%
. 45
33.3%
1 11
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 79
58.5%
. 45
33.3%
1 11
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 79
58.5%
. 45
33.3%
1 11
 
8.1%

posteam
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)4.3%
Missing3
Missing (%)6.0%
Memory size532.0 B
SD
26 
CAR
21 

Length

Max length3
Median length2
Mean length2.4468085
Min length2

Characters and Unicode

Total characters115
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSD
2nd rowSD
3rd rowSD
4th rowSD
5th rowSD

Common Values

ValueCountFrequency (%)
SD 26
52.0%
CAR 21
42.0%
(Missing) 3
 
6.0%

Length

2024-09-06T14:00:38.383999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:38.493352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sd 26
55.3%
car 21
44.7%

Most occurring characters

ValueCountFrequency (%)
S 26
22.6%
D 26
22.6%
C 21
18.3%
A 21
18.3%
R 21
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 26
22.6%
D 26
22.6%
C 21
18.3%
A 21
18.3%
R 21
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 26
22.6%
D 26
22.6%
C 21
18.3%
A 21
18.3%
R 21
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 26
22.6%
D 26
22.6%
C 21
18.3%
A 21
18.3%
R 21
18.3%

DefensiveTeam
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)4.3%
Missing3
Missing (%)6.0%
Memory size532.0 B
CAR
26 
SD
21 

Length

Max length3
Median length3
Mean length2.5531915
Min length2

Characters and Unicode

Total characters120
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAR
2nd rowCAR
3rd rowCAR
4th rowCAR
5th rowCAR

Common Values

ValueCountFrequency (%)
CAR 26
52.0%
SD 21
42.0%
(Missing) 3
 
6.0%

Length

2024-09-06T14:00:38.616188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:38.727468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
car 26
55.3%
sd 21
44.7%

Most occurring characters

ValueCountFrequency (%)
C 26
21.7%
A 26
21.7%
R 26
21.7%
S 21
17.5%
D 21
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 26
21.7%
A 26
21.7%
R 26
21.7%
S 21
17.5%
D 21
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 26
21.7%
A 26
21.7%
R 26
21.7%
S 21
17.5%
D 21
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 26
21.7%
A 26
21.7%
R 26
21.7%
S 21
17.5%
D 21
17.5%

desc
Text

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-09-06T14:00:38.962969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length162
Median length91.5
Mean length80.04
Min length13

Characters and Unicode

Total characters4002
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)96.0%

Sample

1st rowTwo-Minute Warning
2nd row(2:00) (Shotgun) P.Rivers pass deep left to Ty.Williams to CAR 7 for 46 yards (D.Worley). Penalty on CAR-K.Short, Defensive Offside, declined.
3rd row(1:50) (Shotgun) K.Farrow left guard to CAR 9 for -2 yards (V.Butler).
4th row(1:17) (No Huddle, Shotgun) P.Rivers pass incomplete short right.
5th row(1:08) (Shotgun) P.Rivers pass short left to H.Henry for 9 yards, TOUCHDOWN.
ValueCountFrequency (%)
to 48
 
7.4%
yards 36
 
5.5%
for 29
 
4.5%
shotgun 24
 
3.7%
pass 22
 
3.4%
car 20
 
3.1%
sd 18
 
2.8%
left 17
 
2.6%
short 15
 
2.3%
p.rivers 13
 
2.0%
Other values (178) 409
62.8%
2024-09-06T14:00:39.404052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
601
 
15.0%
o 232
 
5.8%
e 218
 
5.4%
t 200
 
5.0%
r 199
 
5.0%
. 170
 
4.2%
a 164
 
4.1%
s 156
 
3.9%
n 138
 
3.4%
d 112
 
2.8%
Other values (62) 1812
45.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
601
 
15.0%
o 232
 
5.8%
e 218
 
5.4%
t 200
 
5.0%
r 199
 
5.0%
. 170
 
4.2%
a 164
 
4.1%
s 156
 
3.9%
n 138
 
3.4%
d 112
 
2.8%
Other values (62) 1812
45.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
601
 
15.0%
o 232
 
5.8%
e 218
 
5.4%
t 200
 
5.0%
r 199
 
5.0%
. 170
 
4.2%
a 164
 
4.1%
s 156
 
3.9%
n 138
 
3.4%
d 112
 
2.8%
Other values (62) 1812
45.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
601
 
15.0%
o 232
 
5.8%
e 218
 
5.4%
t 200
 
5.0%
r 199
 
5.0%
. 170
 
4.2%
a 164
 
4.1%
s 156
 
3.9%
n 138
 
3.4%
d 112
 
2.8%
Other values (62) 1812
45.3%

PlayAttempted
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
1
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 50
100.0%

Length

2024-09-06T14:00:39.569741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:39.677099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 50
100.0%

Most occurring characters

ValueCountFrequency (%)
1 50
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 50
100.0%

Yards.Gained
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.64
Minimum-3
Maximum46
Zeros22
Zeros (%)44.0%
Negative4
Negative (%)8.0%
Memory size532.0 B
2024-09-06T14:00:39.780419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile-1.55
Q10
median0
Q35
95-th percentile24.75
Maximum46
Range49
Interquartile range (IQR)5

Descriptive statistics

Standard deviation10.338673
Coefficient of variation (CV)1.8330981
Kurtosis4.9024689
Mean5.64
Median Absolute Deviation (MAD)2
Skewness2.2093136
Sum282
Variance106.88816
MonotonicityNot monotonic
2024-09-06T14:00:39.908719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 22
44.0%
5 3
 
6.0%
2 3
 
6.0%
3 3
 
6.0%
4 2
 
4.0%
21 2
 
4.0%
9 2
 
4.0%
-2 2
 
4.0%
36 1
 
2.0%
1 1
 
2.0%
Other values (9) 9
18.0%
ValueCountFrequency (%)
-3 1
 
2.0%
-2 2
 
4.0%
-1 1
 
2.0%
0 22
44.0%
1 1
 
2.0%
2 3
 
6.0%
3 3
 
6.0%
4 2
 
4.0%
5 3
 
6.0%
8 1
 
2.0%
ValueCountFrequency (%)
46 1
2.0%
36 1
2.0%
27 1
2.0%
22 1
2.0%
21 2
4.0%
20 1
2.0%
19 1
2.0%
13 1
2.0%
9 2
4.0%
8 1
2.0%

sp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
45 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 45
90.0%
1 5
 
10.0%

Length

2024-09-06T14:00:40.043487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:40.170763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 45
90.0%
1 5
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 45
90.0%
1 5
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 45
90.0%
1 5
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 45
90.0%
1 5
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 45
90.0%
1 5
 
10.0%

Touchdown
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
48 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Length

2024-09-06T14:00:40.297004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:40.403369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

ExPointResult
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing49
Missing (%)98.0%
Memory size532.0 B
2024-09-06T14:00:40.479736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowMade
ValueCountFrequency (%)
made 1
100.0%
2024-09-06T14:00:40.714810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 1
25.0%
a 1
25.0%
d 1
25.0%
e 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1
25.0%
a 1
25.0%
d 1
25.0%
e 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1
25.0%
a 1
25.0%
d 1
25.0%
e 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1
25.0%
a 1
25.0%
d 1
25.0%
e 1
25.0%

TwoPointConv
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size532.0 B

DefTwoPoint
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size532.0 B

Safety
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 50
100.0%

Length

2024-09-06T14:00:40.860068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:40.962369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 50
100.0%

Most occurring characters

ValueCountFrequency (%)
0 50
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Onsidekick
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 50
100.0%

Length

2024-09-06T14:00:41.073592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:41.177831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 50
100.0%

Most occurring characters

ValueCountFrequency (%)
0 50
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50
100.0%

PuntResult
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing49
Missing (%)98.0%
Memory size532.0 B
2024-09-06T14:00:41.257140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowClean
ValueCountFrequency (%)
clean 1
100.0%
2024-09-06T14:00:41.500527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 1
20.0%
l 1
20.0%
e 1
20.0%
a 1
20.0%
n 1
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1
20.0%
l 1
20.0%
e 1
20.0%
a 1
20.0%
n 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1
20.0%
l 1
20.0%
e 1
20.0%
a 1
20.0%
n 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1
20.0%
l 1
20.0%
e 1
20.0%
a 1
20.0%
n 1
20.0%

PlayType
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
Pass
20 
Run
15 
Kickoff
No Play
Field Goal
 
2
Other values (5)

Length

Max length18
Median length11
Mean length5.04
Min length3

Characters and Unicode

Total characters252
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)10.0%

Sample

1st rowTwo Minute Warning
2nd rowPass
3rd rowRun
4th rowPass
5th rowPass

Common Values

ValueCountFrequency (%)
Pass 20
40.0%
Run 15
30.0%
Kickoff 4
 
8.0%
No Play 4
 
8.0%
Field Goal 2
 
4.0%
Two Minute Warning 1
 
2.0%
Extra Point 1
 
2.0%
Quarter End 1
 
2.0%
Timeout 1
 
2.0%
Punt 1
 
2.0%

Length

2024-09-06T14:00:41.668107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:41.821851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pass 20
33.3%
run 15
25.0%
kickoff 4
 
6.7%
no 4
 
6.7%
play 4
 
6.7%
field 2
 
3.3%
goal 2
 
3.3%
two 1
 
1.7%
minute 1
 
1.7%
warning 1
 
1.7%
Other values (6) 6
 
10.0%

Most occurring characters

ValueCountFrequency (%)
s 40
15.9%
a 29
11.5%
P 26
10.3%
n 21
 
8.3%
u 19
 
7.5%
R 15
 
6.0%
o 13
 
5.2%
i 10
 
4.0%
10
 
4.0%
l 8
 
3.2%
Other values (21) 61
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 40
15.9%
a 29
11.5%
P 26
10.3%
n 21
 
8.3%
u 19
 
7.5%
R 15
 
6.0%
o 13
 
5.2%
i 10
 
4.0%
10
 
4.0%
l 8
 
3.2%
Other values (21) 61
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 40
15.9%
a 29
11.5%
P 26
10.3%
n 21
 
8.3%
u 19
 
7.5%
R 15
 
6.0%
o 13
 
5.2%
i 10
 
4.0%
10
 
4.0%
l 8
 
3.2%
Other values (21) 61
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 40
15.9%
a 29
11.5%
P 26
10.3%
n 21
 
8.3%
u 19
 
7.5%
R 15
 
6.0%
o 13
 
5.2%
i 10
 
4.0%
10
 
4.0%
l 8
 
3.2%
Other values (21) 61
24.2%

Passer
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)9.5%
Missing29
Missing (%)58.0%
Memory size532.0 B
P.Rivers
13 
C.Newton

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters168
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP.Rivers
2nd rowP.Rivers
3rd rowP.Rivers
4th rowC.Newton
5th rowC.Newton

Common Values

ValueCountFrequency (%)
P.Rivers 13
26.0%
C.Newton 8
 
16.0%
(Missing) 29
58.0%

Length

2024-09-06T14:00:41.984036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:42.100304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
p.rivers 13
61.9%
c.newton 8
38.1%

Most occurring characters

ValueCountFrequency (%)
. 21
12.5%
e 21
12.5%
P 13
7.7%
R 13
7.7%
i 13
7.7%
v 13
7.7%
r 13
7.7%
s 13
7.7%
C 8
 
4.8%
N 8
 
4.8%
Other values (4) 32
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 21
12.5%
e 21
12.5%
P 13
7.7%
R 13
7.7%
i 13
7.7%
v 13
7.7%
r 13
7.7%
s 13
7.7%
C 8
 
4.8%
N 8
 
4.8%
Other values (4) 32
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 21
12.5%
e 21
12.5%
P 13
7.7%
R 13
7.7%
i 13
7.7%
v 13
7.7%
r 13
7.7%
s 13
7.7%
C 8
 
4.8%
N 8
 
4.8%
Other values (4) 32
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 21
12.5%
e 21
12.5%
P 13
7.7%
R 13
7.7%
i 13
7.7%
v 13
7.7%
r 13
7.7%
s 13
7.7%
C 8
 
4.8%
N 8
 
4.8%
Other values (4) 32
19.0%

Passer_ID
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)10.0%
Missing30
Missing (%)60.0%
Memory size532.0 B
00-0022942
13 
00-0027939

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters200
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00-0022942
2nd row00-0022942
3rd row00-0022942
4th row00-0027939
5th row00-0027939

Common Values

ValueCountFrequency (%)
00-0022942 13
26.0%
00-0027939 7
 
14.0%
(Missing) 30
60.0%

Length

2024-09-06T14:00:42.227226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:42.342523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
00-0022942 13
65.0%
00-0027939 7
35.0%

Most occurring characters

ValueCountFrequency (%)
0 80
40.0%
2 46
23.0%
9 27
 
13.5%
- 20
 
10.0%
4 13
 
6.5%
7 7
 
3.5%
3 7
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 80
40.0%
2 46
23.0%
9 27
 
13.5%
- 20
 
10.0%
4 13
 
6.5%
7 7
 
3.5%
3 7
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 80
40.0%
2 46
23.0%
9 27
 
13.5%
- 20
 
10.0%
4 13
 
6.5%
7 7
 
3.5%
3 7
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 80
40.0%
2 46
23.0%
9 27
 
13.5%
- 20
 
10.0%
4 13
 
6.5%
7 7
 
3.5%
3 7
 
3.5%

PassAttempt
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
29 
1
21 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 29
58.0%
1 21
42.0%

Length

2024-09-06T14:00:42.479733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:42.587028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 29
58.0%
1 21
42.0%

Most occurring characters

ValueCountFrequency (%)
0 29
58.0%
1 21
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 29
58.0%
1 21
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 29
58.0%
1 21
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 29
58.0%
1 21
42.0%

PassOutcome
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)9.5%
Missing29
Missing (%)58.0%
Memory size532.0 B
Complete
12 
Incomplete Pass

Length

Max length15
Median length8
Mean length11
Min length8

Characters and Unicode

Total characters231
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComplete
2nd rowIncomplete Pass
3rd rowComplete
4th rowIncomplete Pass
5th rowComplete

Common Values

ValueCountFrequency (%)
Complete 12
24.0%
Incomplete Pass 9
 
18.0%
(Missing) 29
58.0%

Length

2024-09-06T14:00:42.718326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:42.849168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
complete 12
40.0%
incomplete 9
30.0%
pass 9
30.0%

Most occurring characters

ValueCountFrequency (%)
e 42
18.2%
o 21
9.1%
m 21
9.1%
p 21
9.1%
l 21
9.1%
t 21
9.1%
s 18
7.8%
C 12
 
5.2%
I 9
 
3.9%
n 9
 
3.9%
Other values (4) 36
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 42
18.2%
o 21
9.1%
m 21
9.1%
p 21
9.1%
l 21
9.1%
t 21
9.1%
s 18
7.8%
C 12
 
5.2%
I 9
 
3.9%
n 9
 
3.9%
Other values (4) 36
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 42
18.2%
o 21
9.1%
m 21
9.1%
p 21
9.1%
l 21
9.1%
t 21
9.1%
s 18
7.8%
C 12
 
5.2%
I 9
 
3.9%
n 9
 
3.9%
Other values (4) 36
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 42
18.2%
o 21
9.1%
m 21
9.1%
p 21
9.1%
l 21
9.1%
t 21
9.1%
s 18
7.8%
C 12
 
5.2%
I 9
 
3.9%
n 9
 
3.9%
Other values (4) 36
15.6%

PassLength
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)9.5%
Missing29
Missing (%)58.0%
Memory size532.0 B
Short
15 
Deep

Length

Max length5
Median length5
Mean length4.7142857
Min length4

Characters and Unicode

Total characters99
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeep
2nd rowShort
3rd rowShort
4th rowShort
5th rowDeep

Common Values

ValueCountFrequency (%)
Short 15
30.0%
Deep 6
 
12.0%
(Missing) 29
58.0%

Length

2024-09-06T14:00:42.985410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:43.110729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
short 15
71.4%
deep 6
 
28.6%

Most occurring characters

ValueCountFrequency (%)
S 15
15.2%
h 15
15.2%
o 15
15.2%
r 15
15.2%
t 15
15.2%
e 12
12.1%
D 6
 
6.1%
p 6
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 15
15.2%
h 15
15.2%
o 15
15.2%
r 15
15.2%
t 15
15.2%
e 12
12.1%
D 6
 
6.1%
p 6
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 15
15.2%
h 15
15.2%
o 15
15.2%
r 15
15.2%
t 15
15.2%
e 12
12.1%
D 6
 
6.1%
p 6
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 15
15.2%
h 15
15.2%
o 15
15.2%
r 15
15.2%
t 15
15.2%
e 12
12.1%
D 6
 
6.1%
p 6
 
6.1%

AirYards
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.18
Minimum-5
Maximum43
Zeros31
Zeros (%)62.0%
Negative2
Negative (%)4.0%
Memory size532.0 B
2024-09-06T14:00:43.226601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile0
Q10
median0
Q38
95-th percentile23.4
Maximum43
Range48
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.241353
Coefficient of variation (CV)1.9770951
Kurtosis6.1624538
Mean5.18
Median Absolute Deviation (MAD)0
Skewness2.4163122
Sum259
Variance104.88531
MonotonicityNot monotonic
2024-09-06T14:00:43.353880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 31
62.0%
43 2
 
4.0%
9 2
 
4.0%
5 2
 
4.0%
18 2
 
4.0%
14 2
 
4.0%
11 1
 
2.0%
27 1
 
2.0%
-1 1
 
2.0%
-5 1
 
2.0%
Other values (5) 5
 
10.0%
ValueCountFrequency (%)
-5 1
 
2.0%
-1 1
 
2.0%
0 31
62.0%
3 1
 
2.0%
4 1
 
2.0%
5 2
 
4.0%
9 2
 
4.0%
10 1
 
2.0%
11 1
 
2.0%
13 1
 
2.0%
ValueCountFrequency (%)
43 2
4.0%
27 1
2.0%
19 1
2.0%
18 2
4.0%
14 2
4.0%
13 1
2.0%
11 1
2.0%
10 1
2.0%
9 2
4.0%
5 2
4.0%

YardsAfterCatch
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9
Minimum0
Maximum22
Zeros40
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:43.475163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12.2
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.7947676
Coefficient of variation (CV)2.5235619
Kurtosis8.1936014
Mean1.9
Median Absolute Deviation (MAD)0
Skewness2.8887691
Sum95
Variance22.989796
MonotonicityNot monotonic
2024-09-06T14:00:43.592464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 40
80.0%
3 2
 
4.0%
9 1
 
2.0%
8 1
 
2.0%
22 1
 
2.0%
10 1
 
2.0%
6 1
 
2.0%
14 1
 
2.0%
2 1
 
2.0%
18 1
 
2.0%
ValueCountFrequency (%)
0 40
80.0%
2 1
 
2.0%
3 2
 
4.0%
6 1
 
2.0%
8 1
 
2.0%
9 1
 
2.0%
10 1
 
2.0%
14 1
 
2.0%
18 1
 
2.0%
22 1
 
2.0%
ValueCountFrequency (%)
22 1
 
2.0%
18 1
 
2.0%
14 1
 
2.0%
10 1
 
2.0%
9 1
 
2.0%
8 1
 
2.0%
6 1
 
2.0%
3 2
 
4.0%
2 1
 
2.0%
0 40
80.0%

QBHit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
48 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Length

2024-09-06T14:00:43.719772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:43.826638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 48
96.0%
1 2
 
4.0%

PassLocation
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)14.3%
Missing29
Missing (%)58.0%
Memory size532.0 B
left
11 
right
middle

Length

Max length6
Median length4
Mean length4.6190476
Min length4

Characters and Unicode

Total characters97
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowleft
2nd rowright
3rd rowleft
4th rowleft
5th rowright

Common Values

ValueCountFrequency (%)
left 11
 
22.0%
right 7
 
14.0%
middle 3
 
6.0%
(Missing) 29
58.0%

Length

2024-09-06T14:00:43.959896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:44.098131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
left 11
52.4%
right 7
33.3%
middle 3
 
14.3%

Most occurring characters

ValueCountFrequency (%)
t 18
18.6%
l 14
14.4%
e 14
14.4%
f 11
11.3%
i 10
10.3%
r 7
 
7.2%
g 7
 
7.2%
h 7
 
7.2%
d 6
 
6.2%
m 3
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 18
18.6%
l 14
14.4%
e 14
14.4%
f 11
11.3%
i 10
10.3%
r 7
 
7.2%
g 7
 
7.2%
h 7
 
7.2%
d 6
 
6.2%
m 3
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 18
18.6%
l 14
14.4%
e 14
14.4%
f 11
11.3%
i 10
10.3%
r 7
 
7.2%
g 7
 
7.2%
h 7
 
7.2%
d 6
 
6.2%
m 3
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 18
18.6%
l 14
14.4%
e 14
14.4%
f 11
11.3%
i 10
10.3%
r 7
 
7.2%
g 7
 
7.2%
h 7
 
7.2%
d 6
 
6.2%
m 3
 
3.1%

InterceptionThrown
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 50
100.0%

Length

2024-09-06T14:00:44.232981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:44.336284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 50
100.0%

Most occurring characters

ValueCountFrequency (%)
0 50
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Interceptor
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size532.0 B

Rusher
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)26.7%
Missing35
Missing (%)70.0%
Memory size532.0 B
K.Farrow
J.Stewart
C.Newton
T.Benjamin

Length

Max length10
Median length8
Mean length8.4
Min length8

Characters and Unicode

Total characters126
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)6.7%

Sample

1st rowK.Farrow
2nd rowK.Farrow
3rd rowK.Farrow
4th rowJ.Stewart
5th rowJ.Stewart

Common Values

ValueCountFrequency (%)
K.Farrow 8
 
16.0%
J.Stewart 4
 
8.0%
C.Newton 2
 
4.0%
T.Benjamin 1
 
2.0%
(Missing) 35
70.0%

Length

2024-09-06T14:00:44.464520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:44.605724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
k.farrow 8
53.3%
j.stewart 4
26.7%
c.newton 2
 
13.3%
t.benjamin 1
 
6.7%

Most occurring characters

ValueCountFrequency (%)
r 20
15.9%
. 15
11.9%
w 14
11.1%
a 13
10.3%
t 10
7.9%
o 10
7.9%
K 8
 
6.3%
F 8
 
6.3%
e 7
 
5.6%
J 4
 
3.2%
Other values (9) 17
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 20
15.9%
. 15
11.9%
w 14
11.1%
a 13
10.3%
t 10
7.9%
o 10
7.9%
K 8
 
6.3%
F 8
 
6.3%
e 7
 
5.6%
J 4
 
3.2%
Other values (9) 17
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 20
15.9%
. 15
11.9%
w 14
11.1%
a 13
10.3%
t 10
7.9%
o 10
7.9%
K 8
 
6.3%
F 8
 
6.3%
e 7
 
5.6%
J 4
 
3.2%
Other values (9) 17
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 20
15.9%
. 15
11.9%
w 14
11.1%
a 13
10.3%
t 10
7.9%
o 10
7.9%
K 8
 
6.3%
F 8
 
6.3%
e 7
 
5.6%
J 4
 
3.2%
Other values (9) 17
13.5%

Rusher_ID
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)26.7%
Missing35
Missing (%)70.0%
Memory size532.0 B
00-0032902
00-0026153
00-0027939
00-0029269

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters150
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)6.7%

Sample

1st row00-0032902
2nd row00-0026153
3rd row00-0026153
4th row00-0027939
5th row00-0026153

Common Values

ValueCountFrequency (%)
00-0032902 8
 
16.0%
00-0026153 4
 
8.0%
00-0027939 2
 
4.0%
00-0029269 1
 
2.0%
(Missing) 35
70.0%

Length

2024-09-06T14:00:44.748571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:44.874813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
00-0032902 8
53.3%
00-0026153 4
26.7%
00-0027939 2
 
13.3%
00-0029269 1
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 68
45.3%
2 24
 
16.0%
- 15
 
10.0%
3 14
 
9.3%
9 14
 
9.3%
6 5
 
3.3%
1 4
 
2.7%
5 4
 
2.7%
7 2
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 68
45.3%
2 24
 
16.0%
- 15
 
10.0%
3 14
 
9.3%
9 14
 
9.3%
6 5
 
3.3%
1 4
 
2.7%
5 4
 
2.7%
7 2
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 68
45.3%
2 24
 
16.0%
- 15
 
10.0%
3 14
 
9.3%
9 14
 
9.3%
6 5
 
3.3%
1 4
 
2.7%
5 4
 
2.7%
7 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 68
45.3%
2 24
 
16.0%
- 15
 
10.0%
3 14
 
9.3%
9 14
 
9.3%
6 5
 
3.3%
1 4
 
2.7%
5 4
 
2.7%
7 2
 
1.3%

RushAttempt
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
35 
1
15 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 35
70.0%
1 15
30.0%

Length

2024-09-06T14:00:45.013082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:45.121442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 35
70.0%
1 15
30.0%

Most occurring characters

ValueCountFrequency (%)
0 35
70.0%
1 15
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35
70.0%
1 15
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35
70.0%
1 15
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35
70.0%
1 15
30.0%

RunLocation
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)20.0%
Missing35
Missing (%)70.0%
Memory size532.0 B
left
right
middle

Length

Max length6
Median length5
Mean length4.8666667
Min length4

Characters and Unicode

Total characters73
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmiddle
2nd rowright
3rd rowleft
4th rowleft
5th rowmiddle

Common Values

ValueCountFrequency (%)
left 6
 
12.0%
right 5
 
10.0%
middle 4
 
8.0%
(Missing) 35
70.0%

Length

2024-09-06T14:00:45.258260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:45.394501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
left 6
40.0%
right 5
33.3%
middle 4
26.7%

Most occurring characters

ValueCountFrequency (%)
t 11
15.1%
l 10
13.7%
e 10
13.7%
i 9
12.3%
d 8
11.0%
f 6
8.2%
r 5
6.8%
g 5
6.8%
h 5
6.8%
m 4
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 11
15.1%
l 10
13.7%
e 10
13.7%
i 9
12.3%
d 8
11.0%
f 6
8.2%
r 5
6.8%
g 5
6.8%
h 5
6.8%
m 4
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 11
15.1%
l 10
13.7%
e 10
13.7%
i 9
12.3%
d 8
11.0%
f 6
8.2%
r 5
6.8%
g 5
6.8%
h 5
6.8%
m 4
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 11
15.1%
l 10
13.7%
e 10
13.7%
i 9
12.3%
d 8
11.0%
f 6
8.2%
r 5
6.8%
g 5
6.8%
h 5
6.8%
m 4
 
5.5%

RunGap
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)27.3%
Missing39
Missing (%)78.0%
Memory size532.0 B
tackle
guard
end

Length

Max length6
Median length5
Mean length5.2727273
Min length3

Characters and Unicode

Total characters58
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)9.1%

Sample

1st rowtackle
2nd rowguard
3rd rowguard
4th rowtackle
5th rowguard

Common Values

ValueCountFrequency (%)
tackle 5
 
10.0%
guard 5
 
10.0%
end 1
 
2.0%
(Missing) 39
78.0%

Length

2024-09-06T14:00:45.536305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:45.658585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
tackle 5
45.5%
guard 5
45.5%
end 1
 
9.1%

Most occurring characters

ValueCountFrequency (%)
a 10
17.2%
e 6
10.3%
d 6
10.3%
t 5
8.6%
c 5
8.6%
k 5
8.6%
l 5
8.6%
g 5
8.6%
u 5
8.6%
r 5
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10
17.2%
e 6
10.3%
d 6
10.3%
t 5
8.6%
c 5
8.6%
k 5
8.6%
l 5
8.6%
g 5
8.6%
u 5
8.6%
r 5
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10
17.2%
e 6
10.3%
d 6
10.3%
t 5
8.6%
c 5
8.6%
k 5
8.6%
l 5
8.6%
g 5
8.6%
u 5
8.6%
r 5
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10
17.2%
e 6
10.3%
d 6
10.3%
t 5
8.6%
c 5
8.6%
k 5
8.6%
l 5
8.6%
g 5
8.6%
u 5
8.6%
r 5
8.6%

Receiver
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)47.4%
Missing31
Missing (%)62.0%
Memory size532.0 B
G.Olsen
Ty.Williams
H.Henry
C.Brown
K.Farrow
Other values (4)

Length

Max length11
Median length7
Mean length7.9473684
Min length7

Characters and Unicode

Total characters151
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)10.5%

Sample

1st rowTy.Williams
2nd rowH.Henry
3rd rowG.Olsen
4th rowG.Olsen
5th rowC.Brown

Common Values

ValueCountFrequency (%)
G.Olsen 4
 
8.0%
Ty.Williams 3
 
6.0%
H.Henry 2
 
4.0%
C.Brown 2
 
4.0%
K.Farrow 2
 
4.0%
A.Gates 2
 
4.0%
D.Inman 2
 
4.0%
E.Dickson 1
 
2.0%
J.Stewart 1
 
2.0%
(Missing) 31
62.0%

Length

2024-09-06T14:00:45.803778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:45.968548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
g.olsen 4
21.1%
ty.williams 3
15.8%
h.henry 2
10.5%
c.brown 2
10.5%
k.farrow 2
10.5%
a.gates 2
10.5%
d.inman 2
10.5%
e.dickson 1
 
5.3%
j.stewart 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
. 19
 
12.6%
n 13
 
8.6%
a 10
 
6.6%
l 10
 
6.6%
s 10
 
6.6%
e 9
 
6.0%
r 9
 
6.0%
i 7
 
4.6%
G 6
 
4.0%
y 5
 
3.3%
Other values (20) 53
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 19
 
12.6%
n 13
 
8.6%
a 10
 
6.6%
l 10
 
6.6%
s 10
 
6.6%
e 9
 
6.0%
r 9
 
6.0%
i 7
 
4.6%
G 6
 
4.0%
y 5
 
3.3%
Other values (20) 53
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 19
 
12.6%
n 13
 
8.6%
a 10
 
6.6%
l 10
 
6.6%
s 10
 
6.6%
e 9
 
6.0%
r 9
 
6.0%
i 7
 
4.6%
G 6
 
4.0%
y 5
 
3.3%
Other values (20) 53
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 19
 
12.6%
n 13
 
8.6%
a 10
 
6.6%
l 10
 
6.6%
s 10
 
6.6%
e 9
 
6.0%
r 9
 
6.0%
i 7
 
4.6%
G 6
 
4.0%
y 5
 
3.3%
Other values (20) 53
35.1%

Receiver_ID
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)44.4%
Missing32
Missing (%)64.0%
Memory size532.0 B
00-0025418
00-0032160
00-0033090
00-0031118
00-0032902
Other values (3)

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters180
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.6%

Sample

1st row00-0032160
2nd row00-0033090
3rd row00-0025418
4th row00-0025418
5th row00-0031118

Common Values

ValueCountFrequency (%)
00-0025418 4
 
8.0%
00-0032160 3
 
6.0%
00-0033090 2
 
4.0%
00-0031118 2
 
4.0%
00-0032902 2
 
4.0%
00-0021547 2
 
4.0%
00-0028411 2
 
4.0%
00-0027675 1
 
2.0%
(Missing) 32
64.0%

Length

2024-09-06T14:00:46.147292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:46.299495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
00-0025418 4
22.2%
00-0032160 3
16.7%
00-0033090 2
11.1%
00-0031118 2
11.1%
00-0032902 2
11.1%
00-0021547 2
11.1%
00-0028411 2
11.1%
00-0027675 1
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 81
45.0%
1 19
 
10.6%
- 18
 
10.0%
2 16
 
8.9%
3 11
 
6.1%
4 8
 
4.4%
8 8
 
4.4%
5 7
 
3.9%
6 4
 
2.2%
9 4
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 81
45.0%
1 19
 
10.6%
- 18
 
10.0%
2 16
 
8.9%
3 11
 
6.1%
4 8
 
4.4%
8 8
 
4.4%
5 7
 
3.9%
6 4
 
2.2%
9 4
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 81
45.0%
1 19
 
10.6%
- 18
 
10.0%
2 16
 
8.9%
3 11
 
6.1%
4 8
 
4.4%
8 8
 
4.4%
5 7
 
3.9%
6 4
 
2.2%
9 4
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 81
45.0%
1 19
 
10.6%
- 18
 
10.0%
2 16
 
8.9%
3 11
 
6.1%
4 8
 
4.4%
8 8
 
4.4%
5 7
 
3.9%
6 4
 
2.2%
9 4
 
2.2%

Reception
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
38 
1
12 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Length

2024-09-06T14:00:46.474121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:46.583343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

ReturnResult
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing47
Missing (%)94.0%
Memory size532.0 B
Touchback

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters27
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTouchback
2nd rowTouchback
3rd rowTouchback

Common Values

ValueCountFrequency (%)
Touchback 3
 
6.0%
(Missing) 47
94.0%

Length

2024-09-06T14:00:46.706535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:46.815764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
touchback 3
100.0%

Most occurring characters

ValueCountFrequency (%)
c 6
22.2%
T 3
11.1%
o 3
11.1%
u 3
11.1%
h 3
11.1%
b 3
11.1%
a 3
11.1%
k 3
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 6
22.2%
T 3
11.1%
o 3
11.1%
u 3
11.1%
h 3
11.1%
b 3
11.1%
a 3
11.1%
k 3
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 6
22.2%
T 3
11.1%
o 3
11.1%
u 3
11.1%
h 3
11.1%
b 3
11.1%
a 3
11.1%
k 3
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 6
22.2%
T 3
11.1%
o 3
11.1%
u 3
11.1%
h 3
11.1%
b 3
11.1%
a 3
11.1%
k 3
11.1%

Returner
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing48
Missing (%)96.0%
Memory size532.0 B
2024-09-06T14:00:46.951951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length6.5
Mean length6.5
Min length6

Characters and Unicode

Total characters13
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowJ.Webb
2nd rowI.Burse
ValueCountFrequency (%)
j.webb 1
50.0%
i.burse 1
50.0%
2024-09-06T14:00:47.250694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2
15.4%
e 2
15.4%
b 2
15.4%
J 1
7.7%
W 1
7.7%
I 1
7.7%
B 1
7.7%
u 1
7.7%
r 1
7.7%
s 1
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2
15.4%
e 2
15.4%
b 2
15.4%
J 1
7.7%
W 1
7.7%
I 1
7.7%
B 1
7.7%
u 1
7.7%
r 1
7.7%
s 1
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2
15.4%
e 2
15.4%
b 2
15.4%
J 1
7.7%
W 1
7.7%
I 1
7.7%
B 1
7.7%
u 1
7.7%
r 1
7.7%
s 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2
15.4%
e 2
15.4%
b 2
15.4%
J 1
7.7%
W 1
7.7%
I 1
7.7%
B 1
7.7%
u 1
7.7%
r 1
7.7%
s 1
7.7%

BlockingPlayer
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size532.0 B

Tackler1
Text

MISSING 

Distinct17
Distinct (%)65.4%
Missing24
Missing (%)48.0%
Memory size532.0 B
2024-09-06T14:00:47.418758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.4615385
Min length6

Characters and Unicode

Total characters220
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)38.5%

Sample

1st rowD.Worley
2nd rowV.Butler
3rd rowD.Watt
4th rowT.Palepoi
5th rowT.Palepoi
ValueCountFrequency (%)
d.worley 3
11.5%
k.coleman 3
11.5%
d.perryman 2
 
7.7%
t.boston 2
 
7.7%
s.thompson 2
 
7.7%
t.palepoi 2
 
7.7%
v.butler 2
 
7.7%
c.liuget 1
 
3.8%
l.johnson 1
 
3.8%
f.whittaker 1
 
3.8%
Other values (7) 7
26.9%
2024-09-06T14:00:47.731467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 26
 
11.8%
o 19
 
8.6%
e 18
 
8.2%
a 13
 
5.9%
l 13
 
5.9%
n 13
 
5.9%
r 12
 
5.5%
D 9
 
4.1%
t 9
 
4.1%
m 8
 
3.6%
Other values (24) 80
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 26
 
11.8%
o 19
 
8.6%
e 18
 
8.2%
a 13
 
5.9%
l 13
 
5.9%
n 13
 
5.9%
r 12
 
5.5%
D 9
 
4.1%
t 9
 
4.1%
m 8
 
3.6%
Other values (24) 80
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 26
 
11.8%
o 19
 
8.6%
e 18
 
8.2%
a 13
 
5.9%
l 13
 
5.9%
n 13
 
5.9%
r 12
 
5.5%
D 9
 
4.1%
t 9
 
4.1%
m 8
 
3.6%
Other values (24) 80
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 26
 
11.8%
o 19
 
8.6%
e 18
 
8.2%
a 13
 
5.9%
l 13
 
5.9%
n 13
 
5.9%
r 12
 
5.5%
D 9
 
4.1%
t 9
 
4.1%
m 8
 
3.6%
Other values (24) 80
36.4%

Tackler2
Text

MISSING 

Distinct5
Distinct (%)83.3%
Missing44
Missing (%)88.0%
Memory size532.0 B
2024-09-06T14:00:47.877103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length8.5
Mean length7.6666667
Min length7

Characters and Unicode

Total characters46
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)66.7%

Sample

1st rowJ.Brown
2nd rowT.Davis
3rd rowK.Coleman
4th rowD.Worley
5th rowJ.Addae
ValueCountFrequency (%)
d.worley 2
33.3%
j.brown 1
16.7%
t.davis 1
16.7%
k.coleman 1
16.7%
j.addae 1
16.7%
2024-09-06T14:00:48.184820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 6
13.0%
o 4
 
8.7%
e 4
 
8.7%
D 3
 
6.5%
a 3
 
6.5%
r 3
 
6.5%
l 3
 
6.5%
J 2
 
4.3%
n 2
 
4.3%
y 2
 
4.3%
Other values (12) 14
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 6
13.0%
o 4
 
8.7%
e 4
 
8.7%
D 3
 
6.5%
a 3
 
6.5%
r 3
 
6.5%
l 3
 
6.5%
J 2
 
4.3%
n 2
 
4.3%
y 2
 
4.3%
Other values (12) 14
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 6
13.0%
o 4
 
8.7%
e 4
 
8.7%
D 3
 
6.5%
a 3
 
6.5%
r 3
 
6.5%
l 3
 
6.5%
J 2
 
4.3%
n 2
 
4.3%
y 2
 
4.3%
Other values (12) 14
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 6
13.0%
o 4
 
8.7%
e 4
 
8.7%
D 3
 
6.5%
a 3
 
6.5%
r 3
 
6.5%
l 3
 
6.5%
J 2
 
4.3%
n 2
 
4.3%
y 2
 
4.3%
Other values (12) 14
30.4%

FieldGoalResult
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing48
Missing (%)96.0%
Memory size532.0 B
2024-09-06T14:00:48.298031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
ValueCountFrequency (%)
good 2
100.0%
2024-09-06T14:00:48.534428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4
50.0%
G 2
25.0%
d 2
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4
50.0%
G 2
25.0%
d 2
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4
50.0%
G 2
25.0%
d 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4
50.0%
G 2
25.0%
d 2
25.0%

FieldGoalDistance
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing48
Missing (%)96.0%
Memory size532.0 B
23.0
32.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row23.0
2nd row32.0

Common Values

ValueCountFrequency (%)
23.0 1
 
2.0%
32.0 1
 
2.0%
(Missing) 48
96.0%

Length

2024-09-06T14:00:48.681062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:48.798263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
23.0 1
50.0%
32.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
2 2
25.0%
3 2
25.0%
. 2
25.0%
0 2
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2
25.0%
3 2
25.0%
. 2
25.0%
0 2
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2
25.0%
3 2
25.0%
. 2
25.0%
0 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2
25.0%
3 2
25.0%
. 2
25.0%
0 2
25.0%

Fumble
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 50
100.0%

Length

2024-09-06T14:00:48.922445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:49.025685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 50
100.0%

Most occurring characters

ValueCountFrequency (%)
0 50
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50
100.0%

RecFumbTeam
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size532.0 B

RecFumbPlayer
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size532.0 B

Sack
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 50
100.0%

Length

2024-09-06T14:00:49.137899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:49.241648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 50
100.0%

Most occurring characters

ValueCountFrequency (%)
0 50
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Challenge.Replay
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
50 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 50
100.0%

Length

2024-09-06T14:00:49.352864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:49.456102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 50
100.0%

Most occurring characters

ValueCountFrequency (%)
0 50
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 50
100.0%

ChalReplayResult
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size532.0 B

Accepted.Penalty
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
46 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 46
92.0%
1 4
 
8.0%

Length

2024-09-06T14:00:49.567319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:49.676541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 46
92.0%
1 4
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 46
92.0%
1 4
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 46
92.0%
1 4
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 46
92.0%
1 4
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 46
92.0%
1 4
 
8.0%

PenalizedTeam
Text

MISSING 

Distinct2
Distinct (%)50.0%
Missing46
Missing (%)92.0%
Memory size532.0 B
2024-09-06T14:00:49.772798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Characters and Unicode

Total characters10
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAR
2nd rowSD
3rd rowSD
4th rowCAR
ValueCountFrequency (%)
car 2
50.0%
sd 2
50.0%
2024-09-06T14:00:50.035125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 2
20.0%
A 2
20.0%
R 2
20.0%
S 2
20.0%
D 2
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2
20.0%
A 2
20.0%
R 2
20.0%
S 2
20.0%
D 2
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2
20.0%
A 2
20.0%
R 2
20.0%
S 2
20.0%
D 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2
20.0%
A 2
20.0%
R 2
20.0%
S 2
20.0%
D 2
20.0%

PenaltyType
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing47
Missing (%)94.0%
Memory size532.0 B
2024-09-06T14:00:50.172784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length11
Mean length15
Min length11

Characters and Unicode

Total characters45
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowFalse Start
2nd rowNeutral Zone Infraction
3rd rowFalse Start
ValueCountFrequency (%)
false 2
28.6%
start 2
28.6%
neutral 1
14.3%
zone 1
14.3%
infraction 1
14.3%
2024-09-06T14:00:50.446592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 6
13.3%
a 6
13.3%
e 4
8.9%
4
8.9%
r 4
8.9%
l 3
 
6.7%
n 3
 
6.7%
F 2
 
4.4%
s 2
 
4.4%
S 2
 
4.4%
Other values (8) 9
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 6
13.3%
a 6
13.3%
e 4
8.9%
4
8.9%
r 4
8.9%
l 3
 
6.7%
n 3
 
6.7%
F 2
 
4.4%
s 2
 
4.4%
S 2
 
4.4%
Other values (8) 9
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 6
13.3%
a 6
13.3%
e 4
8.9%
4
8.9%
r 4
8.9%
l 3
 
6.7%
n 3
 
6.7%
F 2
 
4.4%
s 2
 
4.4%
S 2
 
4.4%
Other values (8) 9
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 6
13.3%
a 6
13.3%
e 4
8.9%
4
8.9%
r 4
8.9%
l 3
 
6.7%
n 3
 
6.7%
F 2
 
4.4%
s 2
 
4.4%
S 2
 
4.4%
Other values (8) 9
20.0%

PenalizedPlayer
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing46
Missing (%)92.0%
Memory size532.0 B
2024-09-06T14:00:50.590720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length8.5
Mean length8
Min length7

Characters and Unicode

Total characters32
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowT.Turner
2nd rowC.Liuget
3rd rowA.Gates
4th rowE.Dickson
ValueCountFrequency (%)
t.turner 1
25.0%
c.liuget 1
25.0%
a.gates 1
25.0%
e.dickson 1
25.0%
2024-09-06T14:00:50.894449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 4
 
12.5%
e 3
 
9.4%
T 2
 
6.2%
s 2
 
6.2%
i 2
 
6.2%
t 2
 
6.2%
n 2
 
6.2%
r 2
 
6.2%
u 2
 
6.2%
C 1
 
3.1%
Other values (10) 10
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4
 
12.5%
e 3
 
9.4%
T 2
 
6.2%
s 2
 
6.2%
i 2
 
6.2%
t 2
 
6.2%
n 2
 
6.2%
r 2
 
6.2%
u 2
 
6.2%
C 1
 
3.1%
Other values (10) 10
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4
 
12.5%
e 3
 
9.4%
T 2
 
6.2%
s 2
 
6.2%
i 2
 
6.2%
t 2
 
6.2%
n 2
 
6.2%
r 2
 
6.2%
u 2
 
6.2%
C 1
 
3.1%
Other values (10) 10
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4
 
12.5%
e 3
 
9.4%
T 2
 
6.2%
s 2
 
6.2%
i 2
 
6.2%
t 2
 
6.2%
n 2
 
6.2%
r 2
 
6.2%
u 2
 
6.2%
C 1
 
3.1%
Other values (10) 10
31.2%

Penalty.Yards
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
46 
5
 
3
10
 
1

Length

Max length2
Median length1
Mean length1.02
Min length1

Characters and Unicode

Total characters51
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 46
92.0%
5 3
 
6.0%
10 1
 
2.0%

Length

2024-09-06T14:00:51.051058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:51.166264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 46
92.0%
5 3
 
6.0%
10 1
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 47
92.2%
5 3
 
5.9%
1 1
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 47
92.2%
5 3
 
5.9%
1 1
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 47
92.2%
5 3
 
5.9%
1 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 47
92.2%
5 3
 
5.9%
1 1
 
2.0%

PosTeamScore
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)12.8%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean14.234043
Minimum0
Maximum26
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:51.271497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median10
Q323
95-th percentile26
Maximum26
Range26
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.07508
Coefficient of variation (CV)0.63756167
Kurtosis-1.6803815
Mean14.234043
Median Absolute Deviation (MAD)10
Skewness0.040903724
Sum669
Variance82.357077
MonotonicityNot monotonic
2024-09-06T14:00:51.391689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7 16
32.0%
23 15
30.0%
26 6
 
12.0%
10 5
 
10.0%
0 4
 
8.0%
6 1
 
2.0%
(Missing) 3
 
6.0%
ValueCountFrequency (%)
0 4
 
8.0%
6 1
 
2.0%
7 16
32.0%
10 5
 
10.0%
23 15
30.0%
26 6
 
12.0%
ValueCountFrequency (%)
26 6
 
12.0%
23 15
30.0%
10 5
 
10.0%
7 16
32.0%
6 1
 
2.0%
0 4
 
8.0%

DefTeamScore
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)8.5%
Missing3
Missing (%)6.0%
Memory size532.0 B
26.0
21 
7.0
15 
10.0
23.0

Length

Max length4
Median length4
Mean length3.6808511
Min length3

Characters and Unicode

Total characters173
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row23.0
2nd row23.0
3rd row23.0
4th row23.0
5th row23.0

Common Values

ValueCountFrequency (%)
26.0 21
42.0%
7.0 15
30.0%
10.0 6
 
12.0%
23.0 5
 
10.0%
(Missing) 3
 
6.0%

Length

2024-09-06T14:00:51.527839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:51.651536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
26.0 21
44.7%
7.0 15
31.9%
10.0 6
 
12.8%
23.0 5
 
10.6%

Most occurring characters

ValueCountFrequency (%)
0 53
30.6%
. 47
27.2%
2 26
15.0%
6 21
 
12.1%
7 15
 
8.7%
1 6
 
3.5%
3 5
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 53
30.6%
. 47
27.2%
2 26
15.0%
6 21
 
12.1%
7 15
 
8.7%
1 6
 
3.5%
3 5
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 53
30.6%
. 47
27.2%
2 26
15.0%
6 21
 
12.1%
7 15
 
8.7%
1 6
 
3.5%
3 5
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 53
30.6%
. 47
27.2%
2 26
15.0%
6 21
 
12.1%
7 15
 
8.7%
1 6
 
3.5%
3 5
 
2.9%

ScoreDiff
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)10.6%
Missing3
Missing (%)6.0%
Memory size532.0 B
16.0
21 
-19.0
16 
-16.0
-23.0
-17.0
 
1

Length

Max length5
Median length5
Mean length4.5531915
Min length4

Characters and Unicode

Total characters214
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row-23.0
2nd row-23.0
3rd row-23.0
4th row-23.0
5th row-17.0

Common Values

ValueCountFrequency (%)
16.0 21
42.0%
-19.0 16
32.0%
-16.0 5
 
10.0%
-23.0 4
 
8.0%
-17.0 1
 
2.0%
(Missing) 3
 
6.0%

Length

2024-09-06T14:00:51.788683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:51.909968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
16.0 26
55.3%
19.0 16
34.0%
23.0 4
 
8.5%
17.0 1
 
2.1%

Most occurring characters

ValueCountFrequency (%)
. 47
22.0%
0 47
22.0%
1 43
20.1%
6 26
12.1%
- 26
12.1%
9 16
 
7.5%
2 4
 
1.9%
3 4
 
1.9%
7 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 47
22.0%
0 47
22.0%
1 43
20.1%
6 26
12.1%
- 26
12.1%
9 16
 
7.5%
2 4
 
1.9%
3 4
 
1.9%
7 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 47
22.0%
0 47
22.0%
1 43
20.1%
6 26
12.1%
- 26
12.1%
9 16
 
7.5%
2 4
 
1.9%
3 4
 
1.9%
7 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 47
22.0%
0 47
22.0%
1 43
20.1%
6 26
12.1%
- 26
12.1%
9 16
 
7.5%
2 4
 
1.9%
3 4
 
1.9%
7 1
 
0.5%

AbsScoreDiff
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)8.5%
Missing3
Missing (%)6.0%
Memory size532.0 B
16.0
26 
19.0
16 
23.0
17.0
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters188
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row23.0
2nd row23.0
3rd row23.0
4th row23.0
5th row17.0

Common Values

ValueCountFrequency (%)
16.0 26
52.0%
19.0 16
32.0%
23.0 4
 
8.0%
17.0 1
 
2.0%
(Missing) 3
 
6.0%

Length

2024-09-06T14:00:52.043256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:52.154113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
16.0 26
55.3%
19.0 16
34.0%
23.0 4
 
8.5%
17.0 1
 
2.1%

Most occurring characters

ValueCountFrequency (%)
. 47
25.0%
0 47
25.0%
1 43
22.9%
6 26
13.8%
9 16
 
8.5%
2 4
 
2.1%
3 4
 
2.1%
7 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 47
25.0%
0 47
25.0%
1 43
22.9%
6 26
13.8%
9 16
 
8.5%
2 4
 
2.1%
3 4
 
2.1%
7 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 47
25.0%
0 47
25.0%
1 43
22.9%
6 26
13.8%
9 16
 
8.5%
2 4
 
2.1%
3 4
 
2.1%
7 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 47
25.0%
0 47
25.0%
1 43
22.9%
6 26
13.8%
9 16
 
8.5%
2 4
 
2.1%
3 4
 
2.1%
7 1
 
0.5%

HomeTeam
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
CAR
50 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters150
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAR
2nd rowCAR
3rd rowCAR
4th rowCAR
5th rowCAR

Common Values

ValueCountFrequency (%)
CAR 50
100.0%

Length

2024-09-06T14:00:52.282344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:52.383681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
car 50
100.0%

Most occurring characters

ValueCountFrequency (%)
C 50
33.3%
A 50
33.3%
R 50
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 50
33.3%
A 50
33.3%
R 50
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 50
33.3%
A 50
33.3%
R 50
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 50
33.3%
A 50
33.3%
R 50
33.3%

AwayTeam
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
SD
50 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters100
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSD
2nd rowSD
3rd rowSD
4th rowSD
5th rowSD

Common Values

ValueCountFrequency (%)
SD 50
100.0%

Length

2024-09-06T14:00:52.499915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:52.604289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sd 50
100.0%

Most occurring characters

ValueCountFrequency (%)
S 50
50.0%
D 50
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 50
50.0%
D 50
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 50
50.0%
D 50
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 50
50.0%
D 50
50.0%

Timeout_Indicator
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
49 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49
98.0%
1 1
 
2.0%

Length

2024-09-06T14:00:52.716561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:52.824786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 49
98.0%
1 1
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 49
98.0%
1 1
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 49
98.0%
1 1
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 49
98.0%
1 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 49
98.0%
1 1
 
2.0%

Timeout_Team
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing49
Missing (%)98.0%
Memory size532.0 B
2024-09-06T14:00:52.882147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowSD
ValueCountFrequency (%)
sd 1
100.0%
2024-09-06T14:00:53.103581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1
50.0%
D 1
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1
50.0%
D 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1
50.0%
D 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1
50.0%
D 1
50.0%

posteam_timeouts_pre
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
3
31 
2
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 31
62.0%
2 19
38.0%

Length

2024-09-06T14:00:53.253208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:53.362429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 31
62.0%
2 19
38.0%

Most occurring characters

ValueCountFrequency (%)
3 31
62.0%
2 19
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 31
62.0%
2 19
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 31
62.0%
2 19
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 31
62.0%
2 19
38.0%

HomeTimeouts_Remaining_Pre
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
3
40 
2
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Length

2024-09-06T14:00:53.480628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:53.589850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring characters

ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

AwayTimeouts_Remaining_Pre
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2
25 
3
25 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 25
50.0%
3 25
50.0%

Length

2024-09-06T14:00:53.709045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:53.817270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 25
50.0%
3 25
50.0%

Most occurring characters

ValueCountFrequency (%)
2 25
50.0%
3 25
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 25
50.0%
3 25
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 25
50.0%
3 25
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 25
50.0%
3 25
50.0%

HomeTimeouts_Remaining_Post
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
3
40 
2
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Length

2024-09-06T14:00:53.937464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:54.046686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring characters

ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 40
80.0%
2 10
 
20.0%

AwayTimeouts_Remaining_Post
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2
26 
3
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 26
52.0%
3 24
48.0%

Length

2024-09-06T14:00:54.166392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:54.275614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 26
52.0%
3 24
48.0%

Most occurring characters

ValueCountFrequency (%)
2 26
52.0%
3 24
48.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 26
52.0%
3 24
48.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 26
52.0%
3 24
48.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 26
52.0%
3 24
48.0%

No_Score_Prob
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.065099758
Minimum0
Maximum0.72843605
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:54.404783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00086675085
median0.003542622
Q30.017332969
95-th percentile0.55901399
Maximum0.72843605
Range0.72843605
Interquartile range (IQR)0.016466218

Descriptive statistics

Standard deviation0.17692175
Coefficient of variation (CV)2.7177021
Kurtosis9.2322556
Mean0.065099758
Median Absolute Deviation (MAD)0.003191311
Skewness3.1879327
Sum3.2549879
Variance0.031301305
MonotonicityNot monotonic
2024-09-06T14:00:54.577488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 4
 
8.0%
0.001373679591 2
 
4.0%
0.00320544785 2
 
4.0%
0.0003905116012 2
 
4.0%
0.01733296921 2
 
4.0%
0.02193200185 1
 
2.0%
0.02650233829 1
 
2.0%
0.002251986179 1
 
2.0%
0.002532223674 1
 
2.0%
0.00360131626 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0 4
8.0%
8.007188305 × 10-51
 
2.0%
0.0002843106491 1
 
2.0%
0.0003121104865 1
 
2.0%
0.0003905116012 2
4.0%
0.000448841926 1
 
2.0%
0.0006589525826 1
 
2.0%
0.0007514977634 1
 
2.0%
0.0007857100641 1
 
2.0%
0.001109873223 1
 
2.0%
ValueCountFrequency (%)
0.7284360468 1
2.0%
0.7118550572 1
2.0%
0.6814325379 1
2.0%
0.4093913115 1
2.0%
0.1746726615 1
2.0%
0.1591280565 1
2.0%
0.127422968 1
2.0%
0.02653359266 1
2.0%
0.02650233829 1
2.0%
0.02277567576 1
2.0%

Opp_Field_Goal_Prob
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.078487058
Minimum0
Maximum0.31330159
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:54.750578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.018607918
median0.048747182
Q30.14412206
95-th percentile0.20189993
Maximum0.31330159
Range0.31330159
Interquartile range (IQR)0.12551415

Descriptive statistics

Standard deviation0.074980777
Coefficient of variation (CV)0.95532664
Kurtosis0.71389564
Mean0.078487058
Median Absolute Deviation (MAD)0.039180951
Skewness1.1158315
Sum3.9243529
Variance0.0056221169
MonotonicityNot monotonic
2024-09-06T14:00:54.916282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 4
 
8.0%
0.1626315095 2
 
4.0%
0.1610790146 2
 
4.0%
0.01826518681 2
 
4.0%
0.1562864702 2
 
4.0%
0.1257369502 1
 
2.0%
0.3133015944 1
 
2.0%
0.03504901705 1
 
2.0%
0.04147394056 1
 
2.0%
0.05559275543 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0 4
8.0%
0.003787488778 1
 
2.0%
0.007940225997 1
 
2.0%
0.009097553521 1
 
2.0%
0.01003490992 1
 
2.0%
0.01335565056 1
 
2.0%
0.0134609191 1
 
2.0%
0.01444007498 1
 
2.0%
0.01826518681 2
4.0%
0.01963611107 1
 
2.0%
ValueCountFrequency (%)
0.3133015944 1
2.0%
0.2550860803 1
2.0%
0.203799099 1
2.0%
0.1995787132 1
2.0%
0.1886362112 1
2.0%
0.1626315095 2
4.0%
0.1610790146 2
4.0%
0.1597660036 1
2.0%
0.1562864702 2
4.0%
0.1502504346 1
2.0%

Opp_Safety_Prob
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0018665991
Minimum0
Maximum0.01049431
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:55.085036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.7633479 × 10-5
median0.00027927938
Q30.0031649824
95-th percentile0.0091313151
Maximum0.01049431
Range0.01049431
Interquartile range (IQR)0.003127349

Descriptive statistics

Standard deviation0.0028508395
Coefficient of variation (CV)1.5272908
Kurtosis2.5776123
Mean0.0018665991
Median Absolute Deviation (MAD)0.00027595589
Skewness1.7924549
Sum0.093329954
Variance8.1272861 × 10-6
MonotonicityNot monotonic
2024-09-06T14:00:55.255762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 4
 
8.0%
0.004440726358 2
 
4.0%
0.004384799727 2
 
4.0%
6.790620335 × 10-52
 
4.0%
0.0042281345 2
 
4.0%
0.001971765046 1
 
2.0%
0.009810047817 1
 
2.0%
3.865673059 × 10-51
 
2.0%
9.682435055 × 10-51
 
2.0%
0.0001852741686 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0 4
8.0%
6.646966883 × 10-61
 
2.0%
9.779065469 × 10-61
 
2.0%
1.356354729 × 10-51
 
2.0%
2.080081462 × 10-51
 
2.0%
2.993212882 × 10-51
 
2.0%
3.028574021 × 10-51
 
2.0%
3.283906665 × 10-51
 
2.0%
3.349017409 × 10-51
 
2.0%
3.729239526 × 10-51
 
2.0%
ValueCountFrequency (%)
0.01049431036 1
2.0%
0.01007367504 1
2.0%
0.009810047817 1
2.0%
0.00830175287 1
2.0%
0.005702743382 1
2.0%
0.004440726358 2
4.0%
0.004384799727 2
4.0%
0.0042281345 2
4.0%
0.003372420098 1
2.0%
0.003245977755 1
2.0%

Opp_Touchdown_Prob
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11648976
Minimum0
Maximum0.438363
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:55.419875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.024291602
median0.069513522
Q30.21484548
95-th percentile0.30960862
Maximum0.438363
Range0.438363
Interquartile range (IQR)0.19055388

Descriptive statistics

Standard deviation0.11459828
Coefficient of variation (CV)0.98376265
Kurtosis0.035491439
Mean0.11648976
Median Absolute Deviation (MAD)0.062541272
Skewness0.97638854
Sum5.8244882
Variance0.013132766
MonotonicityNot monotonic
2024-09-06T14:00:55.576684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 4
 
8.0%
0.2541786338 2
 
4.0%
0.2497487727 2
 
4.0%
0.02416818637 2
 
4.0%
0.238467631 2
 
4.0%
0.1908187651 1
 
2.0%
0.4383630041 1
 
2.0%
0.05337695794 1
 
2.0%
0.06454719649 1
 
2.0%
0.09034190364 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0 4
8.0%
0.004433978032 1
 
2.0%
0.005089114842 1
 
2.0%
0.005626829528 1
 
2.0%
0.005943368667 1
 
2.0%
0.0157333869 1
 
2.0%
0.01794357752 1
 
2.0%
0.01992258202 1
 
2.0%
0.02416818637 2
4.0%
0.02466185069 1
 
2.0%
ValueCountFrequency (%)
0.4383630041 1
2.0%
0.3850120746 1
2.0%
0.313595531 1
2.0%
0.3047357353 1
2.0%
0.2955062134 1
2.0%
0.2541786338 2
4.0%
0.2497487727 2
4.0%
0.2445886757 1
2.0%
0.238467631 2
4.0%
0.2228543899 1
2.0%

Field_Goal_Prob
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32098442
Minimum0
Maximum0.98386553
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:55.736822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.21022366
median0.31854957
Q30.41424992
95-th percentile0.59915048
Maximum0.98386553
Range0.98386553
Interquartile range (IQR)0.20402627

Descriptive statistics

Standard deviation0.20423579
Coefficient of variation (CV)0.63627944
Kurtosis2.4649571
Mean0.32098442
Median Absolute Deviation (MAD)0.10742787
Skewness1.0788132
Sum16.049221
Variance0.041712257
MonotonicityNot monotonic
2024-09-06T14:00:55.906562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 4
 
8.0%
0.2330811741 2
 
4.0%
0.2339786811 2
 
4.0%
0.4861943212 2
 
4.0%
0.2332147428 2
 
4.0%
0.2644713709 1
 
2.0%
0.06169987135 1
 
2.0%
0.3754559419 1
 
2.0%
0.42838688 1
 
2.0%
0.5229452662 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0 4
8.0%
0.06169987135 1
 
2.0%
0.09987816436 1
 
2.0%
0.1076626733 1
 
2.0%
0.1257515685 1
 
2.0%
0.1323805827 1
 
2.0%
0.1849332639 1
 
2.0%
0.1914965312 1
 
2.0%
0.1984681365 1
 
2.0%
0.2093256041 1
 
2.0%
ValueCountFrequency (%)
0.9838655277 1
2.0%
0.949796171 1
2.0%
0.6073207075 1
2.0%
0.5891646506 1
2.0%
0.5863118885 1
2.0%
0.5229452662 1
2.0%
0.4861943212 2
4.0%
0.4776380803 1
2.0%
0.4576699088 1
2.0%
0.4539654021 1
2.0%

Safety_Prob
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0025393477
Minimum0
Maximum0.0043737803
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:56.070341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0016614866
median0.003088237
Q30.0036142338
95-th percentile0.0041162334
Maximum0.0043737803
Range0.0043737803
Interquartile range (IQR)0.0019527472

Descriptive statistics

Standard deviation0.0014040822
Coefficient of variation (CV)0.55293025
Kurtosis-0.90123535
Mean0.0025393477
Median Absolute Deviation (MAD)0.00065802593
Skewness-0.74970595
Sum0.12696739
Variance1.9714467 × 10-6
MonotonicityNot monotonic
2024-09-06T14:00:56.261062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 4
 
8.0%
0.003655713027 2
 
4.0%
0.003616879137 2
 
4.0%
0.003123880526 2
 
4.0%
0.003501623389 2
 
4.0%
0.003479171201 1
 
2.0%
0.004335997679 1
 
2.0%
0.00273274242 1
 
2.0%
0.002915243912 1
 
2.0%
0.00422335513 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0 4
8.0%
0.0002294817315 1
 
2.0%
0.0003246657663 1
 
2.0%
0.0004448396635 1
 
2.0%
0.0004629393256 1
 
2.0%
0.0004650969009 1
 
2.0%
0.0004849497998 1
 
2.0%
0.000513231486 1
 
2.0%
0.0005880230903 1
 
2.0%
0.001580451347 1
 
2.0%
ValueCountFrequency (%)
0.00437378033 1
2.0%
0.004335997679 1
2.0%
0.00422335513 1
2.0%
0.003985306875 1
2.0%
0.003947795981 1
2.0%
0.003836998453 1
2.0%
0.00377302373 1
2.0%
0.003731915777 1
2.0%
0.003692749955 1
2.0%
0.003655713027 2
4.0%

Touchdown_Prob
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33453305
Minimum0
Maximum0.63062441
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:56.417233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25311819
median0.34776486
Q30.46779001
95-th percentile0.53955312
Maximum0.63062441
Range0.63062441
Interquartile range (IQR)0.21467181

Descriptive statistics

Standard deviation0.17342987
Coefficient of variation (CV)0.51842371
Kurtosis-0.47578889
Mean0.33453305
Median Absolute Deviation (MAD)0.12002514
Skewness-0.65493021
Sum16.726653
Variance0.030077919
MonotonicityNot monotonic
2024-09-06T14:00:56.584778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 4
 
8.0%
0.3406385636 2
 
4.0%
0.3439864048 2
 
4.0%
0.4677900073 2
 
4.0%
0.3469684289 2
 
4.0%
0.3915899757 1
 
2.0%
0.1459871464 1
 
2.0%
0.5310946978 1
 
2.0%
0.460047691 1
 
2.0%
0.3231101292 1
 
2.0%
Other values (33) 33
66.0%
ValueCountFrequency (%)
0 4
8.0%
0.006341460631 1
 
2.0%
0.01528202708 1
 
2.0%
0.09379026836 1
 
2.0%
0.09647046164 1
 
2.0%
0.1121739928 1
 
2.0%
0.1459871464 1
 
2.0%
0.1861195791 1
 
2.0%
0.2195635472 1
 
2.0%
0.2520577611 1
 
2.0%
ValueCountFrequency (%)
0.6306244131 1
2.0%
0.6092730388 1
2.0%
0.5462259608 1
2.0%
0.5313974175 1
2.0%
0.5310946978 1
2.0%
0.4994077708 1
2.0%
0.49023069 1
2.0%
0.486453019 1
2.0%
0.4782903869 1
2.0%
0.4766095152 1
2.0%

ExPoint_Prob
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0.0
49 
0.931114949971538
 
1

Length

Max length17
Median length3
Mean length3.28
Min length3

Characters and Unicode

Total characters164
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49
98.0%
0.931114949971538 1
 
2.0%

Length

2024-09-06T14:00:56.746861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:56.860580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49
98.0%
0.931114949971538 1
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 99
60.4%
. 50
30.5%
9 4
 
2.4%
1 4
 
2.4%
3 2
 
1.2%
4 2
 
1.2%
7 1
 
0.6%
5 1
 
0.6%
8 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 99
60.4%
. 50
30.5%
9 4
 
2.4%
1 4
 
2.4%
3 2
 
1.2%
4 2
 
1.2%
7 1
 
0.6%
5 1
 
0.6%
8 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 99
60.4%
. 50
30.5%
9 4
 
2.4%
1 4
 
2.4%
3 2
 
1.2%
4 2
 
1.2%
7 1
 
0.6%
5 1
 
0.6%
8 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 99
60.4%
. 50
30.5%
9 4
 
2.4%
1 4
 
2.4%
3 2
 
1.2%
4 2
 
1.2%
7 1
 
0.6%
5 1
 
0.6%
8 1
 
0.6%

TwoPoint_Prob
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0.0
50 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 50
100.0%

Length

2024-09-06T14:00:56.978780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:00:57.083079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 50
100.0%

Most occurring characters

ValueCountFrequency (%)
0 100
66.7%
. 50
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 100
66.7%
. 50
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 100
66.7%
. 50
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 100
66.7%
. 50
33.3%

ExpPts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2737629
Minimum-2.8123843
Maximum5.2529827
Zeros3
Zeros (%)6.0%
Negative4
Negative (%)8.0%
Memory size532.0 B
2024-09-06T14:00:57.205394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2.8123843
5-th percentile-0.40430612
Q10.8304555
median2.8232977
Q33.9038632
95-th percentile4.6000525
Maximum5.2529827
Range8.065367
Interquartile range (IQR)3.0734077

Descriptive statistics

Standard deviation1.9059493
Coefficient of variation (CV)0.83823571
Kurtosis-0.44929583
Mean2.2737629
Median Absolute Deviation (MAD)1.603027
Skewness-0.46697603
Sum113.68815
Variance3.6326426
MonotonicityNot monotonic
2024-09-06T14:00:57.378031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 3
 
6.0%
0.8149984757 2
 
4.0%
0.8768265832 2
 
4.0%
4.515252098 2
 
4.0%
0.9888373804 2
 
4.0%
1.824616548 1
 
2.0%
-2.812384273 1
 
2.0%
4.370633125 1
 
2.0%
3.934879119 1
 
2.0%
4.381649514 1
 
2.0%
Other values (34) 34
68.0%
ValueCountFrequency (%)
-2.812384273 1
 
2.0%
-1.772605021 1
 
2.0%
-0.4471196338 1
 
2.0%
-0.3519784998 1
 
2.0%
0 3
6.0%
0.04361855745 1
 
2.0%
0.6015935719 1
 
2.0%
0.6166137652 1
 
2.0%
0.7944250441 1
 
2.0%
0.8149984757 2
4.0%
ValueCountFrequency (%)
5.252982739 1
2.0%
5.196526153 1
2.0%
4.66943469 1
2.0%
4.515252098 2
4.0%
4.470999909 1
2.0%
4.381649514 1
2.0%
4.370633125 1
2.0%
4.354222885 1
2.0%
4.145226759 1
2.0%
4.112671035 1
2.0%

EPA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25889129
Minimum-1.5696949
Maximum3.7661592
Zeros5
Zeros (%)10.0%
Negative24
Negative (%)48.0%
Memory size532.0 B
2024-09-06T14:00:57.553079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.5696949
5-th percentile-1.1969176
Q1-0.48454502
median0
Q30.57407615
95-th percentile2.7587715
Maximum3.7661592
Range5.335854
Interquartile range (IQR)1.0586212

Descriptive statistics

Standard deviation1.1908367
Coefficient of variation (CV)4.5997556
Kurtosis1.4467048
Mean0.25889129
Median Absolute Deviation (MAD)0.559694
Skewness1.2590252
Sum12.944564
Variance1.4180919
MonotonicityNot monotonic
2024-09-06T14:00:57.725644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 5
 
10.0%
1.829563799 1
 
2.0%
0.2229310523 1
 
2.0%
-0.2236803134 1
 
2.0%
1.724786936 1
 
2.0%
-0.4357540056 1
 
2.0%
-0.895367846 1
 
2.0%
1.629923417 1
 
2.0%
-0.3152118049 1
 
2.0%
-0.2415518496 1
 
2.0%
Other values (36) 36
72.0%
ValueCountFrequency (%)
-1.56969488 1
2.0%
-1.34081588 1
2.0%
-1.325485387 1
2.0%
-1.039779252 1
2.0%
-0.895367846 1
2.0%
-0.7713799183 1
2.0%
-0.7604643963 1
2.0%
-0.7377306404 1
2.0%
-0.6601844609 1
2.0%
-0.6015935719 1
2.0%
ValueCountFrequency (%)
3.76615916 1
2.0%
3.562581914 1
2.0%
2.854773241 1
2.0%
2.64143611 1
2.0%
1.829563799 1
2.0%
1.724786936 1
2.0%
1.629923417 1
2.0%
1.477171761 1
2.0%
1.426176855 1
2.0%
1.413426107 1
2.0%

airEPA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing30
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean1.5286851
Minimum-1.951897
Maximum5.4116891
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)12.0%
Memory size532.0 B
2024-09-06T14:00:57.866296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.951897
5-th percentile-1.4958167
Q1-0.23513995
median1.5502445
Q32.9212906
95-th percentile3.8484357
Maximum5.4116891
Range7.363586
Interquartile range (IQR)3.1564306

Descriptive statistics

Standard deviation1.9492086
Coefficient of variation (CV)1.2750884
Kurtosis-0.61574021
Mean1.5286851
Median Absolute Deviation (MAD)1.5559356
Skewness-0.05731638
Sum30.573701
Variance3.7994141
MonotonicityNot monotonic
2024-09-06T14:00:58.003443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-0.2556032607 1
 
2.0%
-0.2283188511 1
 
2.0%
1.353471708 1
 
2.0%
5.411689076 1
 
2.0%
3.647793361 1
 
2.0%
2.887328965 1
 
2.0%
-0.3553281649 1
 
2.0%
0.7982755797 1
 
2.0%
1.169522107 1
 
2.0%
-1.471812501 1
 
2.0%
Other values (10) 10
 
20.0%
(Missing) 30
60.0%
ValueCountFrequency (%)
-1.951896953 1
2.0%
-1.471812501 1
2.0%
-0.8286305181 1
2.0%
-0.3553281649 1
2.0%
-0.2556032607 1
2.0%
-0.2283188511 1
2.0%
0.7982755797 1
2.0%
1.021191446 1
2.0%
1.169522107 1
2.0%
1.353471708 1
2.0%
ValueCountFrequency (%)
5.411689076 1
2.0%
3.76615916 1
2.0%
3.647793361 1
2.0%
3.189184552 1
2.0%
3.02317563 1
2.0%
2.887328965 1
2.0%
2.854773241 1
2.0%
2.484747902 1
2.0%
2.310961386 1
2.0%
1.747017261 1
2.0%

yacEPA
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)95.0%
Missing30
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean-0.73654449
Minimum-6.4514683
Maximum3.3780738
Zeros2
Zeros (%)4.0%
Negative8
Negative (%)16.0%
Memory size532.0 B
2024-09-06T14:00:58.140590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-6.4514683
5-th percentile-4.3343579
Q1-2.7755093
median0.061850027
Q31.3956418
95-th percentile2.0548927
Maximum3.3780738
Range9.8295421
Interquartile range (IQR)4.171151

Descriptive statistics

Standard deviation2.601594
Coefficient of variation (CV)-3.5321613
Kurtosis-0.45842421
Mean-0.73654449
Median Absolute Deviation (MAD)1.5423805
Skewness-0.60695439
Sum-14.73089
Variance6.7682911
MonotonicityNot monotonic
2024-09-06T14:00:58.271269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2
 
4.0%
1.389972818 1
 
2.0%
0.1237000537 1
 
2.0%
-6.451468328 1
 
2.0%
-4.222931052 1
 
2.0%
-3.647793361 1
 
2.0%
1.985251582 1
 
2.0%
-1.234029585 1
 
2.0%
0.555264829 1
 
2.0%
1.566716012 1
 
2.0%
Other values (9) 9
 
18.0%
(Missing) 30
60.0%
ValueCountFrequency (%)
-6.451468328 1
2.0%
-4.222931052 1
2.0%
-4.054442782 1
2.0%
-3.76615916 1
2.0%
-3.647793361 1
2.0%
-2.484747902 1
2.0%
-1.792571364 1
2.0%
-1.234029585 1
2.0%
0 2
4.0%
0.1237000537 1
2.0%
ValueCountFrequency (%)
3.378073808 1
2.0%
1.985251582 1
2.0%
1.641744958 1
2.0%
1.566716012 1
2.0%
1.412648619 1
2.0%
1.389972818 1
2.0%
0.555264829 1
2.0%
0.5394062844 1
2.0%
0.330474724 1
2.0%
0.1237000537 1
2.0%

Home_WP_pre
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)91.5%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean0.92887536
Minimum0.8756083
Maximum0.97296072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:58.418390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.8756083
5-th percentile0.88586587
Q10.91380213
median0.92567912
Q30.94485369
95-th percentile0.96758914
Maximum0.97296072
Range0.097352421
Interquartile range (IQR)0.031051559

Descriptive statistics

Standard deviation0.02437428
Coefficient of variation (CV)0.026240636
Kurtosis-0.40743141
Mean0.92887536
Median Absolute Deviation (MAD)0.016024536
Skewness-0.067606094
Sum43.657142
Variance0.00059410553
MonotonicityNot monotonic
2024-09-06T14:00:58.583975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.9255971343 2
 
4.0%
0.9421321574 2
 
4.0%
0.9665713935 2
 
4.0%
0.9323766117 2
 
4.0%
0.908726062 1
 
2.0%
0.8839675124 1
 
2.0%
0.9095174325 1
 
2.0%
0.9147406355 1
 
2.0%
0.8756083009 1
 
2.0%
0.9256791201 1
 
2.0%
Other values (33) 33
66.0%
(Missing) 3
 
6.0%
ValueCountFrequency (%)
0.8756083009 1
2.0%
0.8812181164 1
2.0%
0.8839675124 1
2.0%
0.8902953626 1
2.0%
0.896019581 1
2.0%
0.8990698834 1
2.0%
0.9085108674 1
2.0%
0.908726062 1
2.0%
0.9095174325 1
2.0%
0.9100002669 1
2.0%
ValueCountFrequency (%)
0.9729607221 1
2.0%
0.9723647489 1
2.0%
0.9680253237 1
2.0%
0.9665713935 2
4.0%
0.9618302716 1
2.0%
0.9590398655 1
2.0%
0.9571647426 1
2.0%
0.9536005064 1
2.0%
0.9528595474 1
2.0%
0.9508507093 1
2.0%

Away_WP_pre
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)91.5%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean0.071124639
Minimum0.027039278
Maximum0.1243917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:58.745059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.027039278
5-th percentile0.032410855
Q10.055146309
median0.07432088
Q30.086197868
95-th percentile0.11413413
Maximum0.1243917
Range0.097352421
Interquartile range (IQR)0.031051559

Descriptive statistics

Standard deviation0.02437428
Coefficient of variation (CV)0.34269812
Kurtosis-0.40743141
Mean0.071124639
Median Absolute Deviation (MAD)0.016024536
Skewness0.067606094
Sum3.342858
Variance0.00059410553
MonotonicityNot monotonic
2024-09-06T14:00:58.906654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.07440286566 2
 
4.0%
0.05786784264 2
 
4.0%
0.03342860653 2
 
4.0%
0.06762338832 2
 
4.0%
0.091273938 1
 
2.0%
0.1160324876 1
 
2.0%
0.09048256753 1
 
2.0%
0.08525936452 1
 
2.0%
0.1243916991 1
 
2.0%
0.07432087987 1
 
2.0%
Other values (33) 33
66.0%
(Missing) 3
 
6.0%
ValueCountFrequency (%)
0.02703927787 1
2.0%
0.02763525108 1
2.0%
0.03197467634 1
2.0%
0.03342860653 2
4.0%
0.03816972838 1
2.0%
0.04096013446 1
2.0%
0.0428352574 1
2.0%
0.04639949363 1
2.0%
0.04714045259 1
2.0%
0.04914929068 1
2.0%
ValueCountFrequency (%)
0.1243916991 1
2.0%
0.1187818836 1
2.0%
0.1160324876 1
2.0%
0.1097046374 1
2.0%
0.103980419 1
2.0%
0.1009301166 1
2.0%
0.0914891326 1
2.0%
0.091273938 1
2.0%
0.09048256753 1
2.0%
0.08999973312 1
2.0%

Home_WP_post
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)93.5%
Missing4
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean0.92583477
Minimum0.82487967
Maximum0.97296072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:59.058764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.82487967
5-th percentile0.88190547
Q10.91248137
median0.92563813
Q30.94213216
95-th percentile0.96657139
Maximum0.97296072
Range0.14808106
Interquartile range (IQR)0.029650789

Descriptive statistics

Standard deviation0.028353018
Coefficient of variation (CV)0.030624274
Kurtosis2.3524423
Mean0.92583477
Median Absolute Deviation (MAD)0.016093112
Skewness-0.91382469
Sum42.588399
Variance0.00080389362
MonotonicityNot monotonic
2024-09-06T14:00:59.231328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.9665713935 2
 
4.0%
0.9421321574 2
 
4.0%
0.9323766117 2
 
4.0%
0.896019581 1
 
2.0%
0.9128636284 1
 
2.0%
0.9095174325 1
 
2.0%
0.9147406355 1
 
2.0%
0.9256791201 1
 
2.0%
0.8812181164 1
 
2.0%
0.9085108674 1
 
2.0%
Other values (33) 33
66.0%
(Missing) 4
 
8.0%
ValueCountFrequency (%)
0.824879665 1
2.0%
0.8756083009 1
2.0%
0.8812181164 1
2.0%
0.8839675124 1
2.0%
0.8902953626 1
2.0%
0.896019581 1
2.0%
0.8990698834 1
2.0%
0.9085108674 1
2.0%
0.908726062 1
2.0%
0.9095174325 1
2.0%
ValueCountFrequency (%)
0.9729607221 1
2.0%
0.9723647489 1
2.0%
0.9665713935 2
4.0%
0.9618302716 1
2.0%
0.9590398655 1
2.0%
0.9571647426 1
2.0%
0.9536005064 1
2.0%
0.9528595474 1
2.0%
0.9508507093 1
2.0%
0.9475752237 1
2.0%

Away_WP_post
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)93.5%
Missing4
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean0.074165235
Minimum0.027039278
Maximum0.17512033
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:59.399906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.027039278
5-th percentile0.033428607
Q10.057867843
median0.074361873
Q30.087518632
95-th percentile0.11809453
Maximum0.17512033
Range0.14808106
Interquartile range (IQR)0.029650789

Descriptive statistics

Standard deviation0.028353018
Coefficient of variation (CV)0.38229526
Kurtosis2.3524423
Mean0.074165235
Median Absolute Deviation (MAD)0.016093112
Skewness0.91382469
Sum3.4116008
Variance0.00080389362
MonotonicityNot monotonic
2024-09-06T14:00:59.573469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.03342860653 2
 
4.0%
0.05786784264 2
 
4.0%
0.06762338832 2
 
4.0%
0.103980419 1
 
2.0%
0.08713637161 1
 
2.0%
0.09048256753 1
 
2.0%
0.08525936452 1
 
2.0%
0.07432087987 1
 
2.0%
0.1187818836 1
 
2.0%
0.0914891326 1
 
2.0%
Other values (33) 33
66.0%
(Missing) 4
 
8.0%
ValueCountFrequency (%)
0.02703927787 1
2.0%
0.02763525108 1
2.0%
0.03342860653 2
4.0%
0.03816972838 1
2.0%
0.04096013446 1
2.0%
0.0428352574 1
2.0%
0.04639949363 1
2.0%
0.04714045259 1
2.0%
0.04914929068 1
2.0%
0.05242477634 1
2.0%
ValueCountFrequency (%)
0.175120335 1
2.0%
0.1243916991 1
2.0%
0.1187818836 1
2.0%
0.1160324876 1
2.0%
0.1097046374 1
2.0%
0.103980419 1
2.0%
0.1009301166 1
2.0%
0.0914891326 1
2.0%
0.091273938 1
2.0%
0.09048256753 1
2.0%

Win_Prob
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43424247
Minimum0
Maximum0.97296072
Zeros3
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-09-06T14:00:59.749647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.014388604
Q10.071866422
median0.097734776
Q30.92559713
95-th percentile0.96657139
Maximum0.97296072
Range0.97296072
Interquartile range (IQR)0.85373071

Descriptive statistics

Standard deviation0.43344909
Coefficient of variation (CV)0.99817297
Kurtosis-1.9517306
Mean0.43424247
Median Absolute Deviation (MAD)0.058547691
Skewness0.32867398
Sum21.712123
Variance0.18787812
MonotonicityNot monotonic
2024-09-06T14:00:59.917423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 3
 
6.0%
0.9255971343 2
 
4.0%
0.05786784264 2
 
4.0%
0.9665713935 2
 
4.0%
0.9323766117 2
 
4.0%
0.091273938 1
 
2.0%
0.8839675124 1
 
2.0%
0.09048256753 1
 
2.0%
0.08525936452 1
 
2.0%
0.1243916991 1
 
2.0%
Other values (34) 34
68.0%
ValueCountFrequency (%)
0 3
6.0%
0.03197467634 1
 
2.0%
0.04639949363 1
 
2.0%
0.05242477634 1
 
2.0%
0.05786784264 2
4.0%
0.05829634359 1
 
2.0%
0.059829722 1
 
2.0%
0.06325064236 1
 
2.0%
0.06769245717 1
 
2.0%
0.07167933954 1
 
2.0%
ValueCountFrequency (%)
0.9729607221 1
2.0%
0.9723647489 1
2.0%
0.9665713935 2
4.0%
0.9618302716 1
2.0%
0.9590398655 1
2.0%
0.9571647426 1
2.0%
0.9528595474 1
2.0%
0.9508507093 1
2.0%
0.9323766117 2
4.0%
0.9270972289 1
2.0%

WPA
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct44
Distinct (%)91.7%
Missing2
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean0.0026153044
Minimum-0.016630244
Maximum0.056338451
Zeros5
Zeros (%)10.0%
Negative25
Negative (%)50.0%
Memory size532.0 B
2024-09-06T14:01:00.074233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.016630244
5-th percentile-0.015504275
Q1-0.0061162942
median-0.00098573014
Q30.0052608036
95-th percentile0.03638491
Maximum0.056338451
Range0.072968696
Interquartile range (IQR)0.011377098

Descriptive statistics

Standard deviation0.015551542
Coefficient of variation (CV)5.9463603
Kurtosis2.734369
Mean0.0026153044
Median Absolute Deviation (MAD)0.0056467925
Skewness1.5926149
Sum0.12553461
Variance0.00024185047
MonotonicityNot monotonic
2024-09-06T14:01:00.239406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 5
 
10.0%
-0.00405595122 1
 
2.0%
-0.003360324815 1
 
2.0%
0.01805489813 1
 
2.0%
-0.005223203003 1
 
2.0%
-0.01093848465 1
 
2.0%
0.01716825272 1
 
2.0%
-0.001489399474 1
 
2.0%
-0.002863361508 1
 
2.0%
-0.008904568833 1
 
2.0%
Other values (34) 34
68.0%
(Missing) 2
 
4.0%
ValueCountFrequency (%)
-0.01663024447 1
2.0%
-0.01619442297 1
2.0%
-0.01572068415 1
2.0%
-0.01510237101 1
2.0%
-0.01474328055 1
2.0%
-0.01093848465 1
2.0%
-0.00941030488 1
2.0%
-0.008904568833 1
2.0%
-0.008718551962 1
2.0%
-0.007404945663 1
2.0%
ValueCountFrequency (%)
0.05633845134 1
2.0%
0.04124655803 1
2.0%
0.04097791315 1
2.0%
0.02785504566 1
2.0%
0.02475854958 1
2.0%
0.02041128008 1
2.0%
0.01843069938 1
2.0%
0.01805489813 1
2.0%
0.01716825272 1
2.0%
0.01332488337 1
2.0%

airWPA
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing30
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean0.018548961
Minimum-0.021246454
Maximum0.063149677
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)12.0%
Memory size532.0 B
2024-09-06T14:01:00.389233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.021246454
5-th percentile-0.014931818
Q1-0.0029297278
median0.015177778
Q30.036884503
95-th percentile0.056679013
Maximum0.063149677
Range0.084396131
Interquartile range (IQR)0.039814231

Descriptive statistics

Standard deviation0.023964967
Coefficient of variation (CV)1.2919844
Kurtosis-0.83594729
Mean0.018548961
Median Absolute Deviation (MAD)0.019577989
Skewness0.19050262
Sum0.37097921
Variance0.00057431965
MonotonicityNot monotonic
2024-09-06T14:01:00.523489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-0.002799828848 1
 
2.0%
-0.003319424617 1
 
2.0%
0.01756978962 1
 
2.0%
0.06314967697 1
 
2.0%
0.04776604357 1
 
2.0%
0.03912989541 1
 
2.0%
-0.003920619309 1
 
2.0%
0.009598814157 1
 
2.0%
0.01278576649 1
 
2.0%
-0.01459946912 1
 
2.0%
Other values (10) 10
 
20.0%
(Missing) 30
60.0%
ValueCountFrequency (%)
-0.02124645368 1
2.0%
-0.01459946912 1
2.0%
-0.009061070512 1
2.0%
-0.003920619309 1
2.0%
-0.003319424617 1
2.0%
-0.002799828848 1
2.0%
0.009598814157 1
2.0%
0.01206040361 1
2.0%
0.01210358828 1
2.0%
0.01278576649 1
2.0%
ValueCountFrequency (%)
0.06314967697 1
2.0%
0.05633845134 1
2.0%
0.04776604357 1
2.0%
0.04124655803 1
2.0%
0.03912989541 1
2.0%
0.03613603875 1
2.0%
0.03523535855 1
2.0%
0.02429859217 1
2.0%
0.01850710158 1
2.0%
0.01756978962 1
2.0%

yacWPA
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)95.0%
Missing30
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean-0.0086144821
Minimum-0.078252048
Maximum0.034571337
Zeros2
Zeros (%)4.0%
Negative8
Negative (%)16.0%
Memory size532.0 B
2024-09-06T14:01:00.655744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.078252048
5-th percentile-0.055515184
Q1-0.024659978
median0.00043045488
Q30.014557597
95-th percentile0.024272736
Maximum0.034571337
Range0.11282339
Interquartile range (IQR)0.039217574

Descriptive statistics

Standard deviation0.029745866
Coefficient of variation (CV)-3.4530069
Kurtosis0.0057361179
Mean-0.0086144821
Median Absolute Deviation (MAD)0.017087922
Skewness-0.80692677
Sum-0.17228964
Variance0.00088481654
MonotonicityNot monotonic
2024-09-06T14:01:00.792598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2
 
4.0%
0.01440080544 1
 
2.0%
0.0008609097519 1
 
2.0%
-0.07825204799 1
 
2.0%
-0.05431850681 1
 
2.0%
-0.04803446425 1
 
2.0%
0.02108887203 1
 
2.0%
-0.01482201716 1
 
2.0%
0.005269131634 1
 
2.0%
0.01502797008 1
 
2.0%
Other values (9) 9
 
18.0%
(Missing) 30
60.0%
ValueCountFrequency (%)
-0.07825204799 1
2.0%
-0.05431850681 1
2.0%
-0.04803446425 1
2.0%
-0.04216132146 1
2.0%
-0.03422778573 1
2.0%
-0.02147070849 1
2.0%
-0.01849291693 1
2.0%
-0.01482201716 1
2.0%
0 2
4.0%
0.0008609097519 1
2.0%
ValueCountFrequency (%)
0.03457133705 1
2.0%
0.0237307047 1
2.0%
0.02108887203 1
2.0%
0.01524138864 1
2.0%
0.01502797008 1
2.0%
0.01440080544 1
2.0%
0.005742554595 1
2.0%
0.005269131634 1
2.0%
0.003556453493 1
2.0%
0.0008609097519 1
2.0%

Season
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2016
50 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 50
100.0%

Length

2024-09-06T14:01:00.930839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T14:01:01.033144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2016 50
100.0%

Most occurring characters

ValueCountFrequency (%)
2 50
25.0%
0 50
25.0%
1 50
25.0%
6 50
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 50
25.0%
0 50
25.0%
1 50
25.0%
6 50
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 50
25.0%
0 50
25.0%
1 50
25.0%
6 50
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 50
25.0%
0 50
25.0%
1 50
25.0%
6 50
25.0%

Interactions

2024-09-06T14:00:27.100412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:48.419013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:51.823618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:54.878288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.397126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:01.967388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:05.429115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:09.110550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.458452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:15.786242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:19.241300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:22.195077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:25.110303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:28.090018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:32.303213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:35.620861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:38.794128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:41.812635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:44.856948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:49.165853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:52.426875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.589964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.586540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.664355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.517133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:08.496721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:11.430951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:14.712921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:17.971779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:20.954758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:24.185737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:27.197735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:48.649012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:51.919940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:54.970598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.498028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:02.102543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:05.529361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:09.212822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.546728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:15.877001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:19.340656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:22.285445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:25.203668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:28.191262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:32.403491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:35.726093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:38.886428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:41.907895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:44.951697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:49.306951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:52.527122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.681234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.683283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.753729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.612369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:08.590977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:11.535285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:14.816159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:18.065169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:21.055103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:24.292091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:27.298081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:48.748395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:52.022251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:55.069906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.605318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:02.248186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:05.638649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:09.322012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.641988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:15.977310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:19.446947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:22.385706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:25.301410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:28.301485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:32.510688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:35.834829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:38.987638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:42.019110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:45.055340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:49.446199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:52.635346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.781481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.783529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.847091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.708717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:08.687332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:11.643574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:14.924467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:18.166512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:21.162459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:24.387412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:27.391406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:48.836758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:52.114975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:55.155281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.697554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:02.376362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:06.226182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:09.421262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.726276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:16.064590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:19.535327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:22.472980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:25.391318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:28.398739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:32.608939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:35.928580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:39.077911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:42.111378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:45.146099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:49.547448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:52.730605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.870308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.877791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.932415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.796552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:08.775701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:11.742917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:15.023736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:18.256880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:21.258805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:24.474789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:27.488758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:48.928094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:52.212294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:55.247521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.790819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:02.505550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:06.342388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:09.521507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.813559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:16.154861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:19.625666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:22.566240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:25.485572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:28.499986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:32.708188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:36.031332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:39.177257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:42.205639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:45.242076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:49.647732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:52.830851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.962540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.973051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:02.022781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.889905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:08.868031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:11.847246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:15.128103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:18.351210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:21.358120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:24.568089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:27.580093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:49.014443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:52.302661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:55.333925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.878100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:02.629731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:06.436680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:09.618760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.895853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:16.240147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:19.712955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:22.652518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:25.573845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-09-06T14:00:03.861662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:07.771471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:10.747507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:13.952787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:17.218989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:20.256505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:23.401377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:26.438907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:30.762769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:51.216788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:54.251507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:57.789231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:01.090810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:04.838612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:08.471108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:11.811134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:15.210692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:18.640300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:21.602651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:24.512810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:27.479163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:31.645887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:34.943374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:38.167756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:41.186225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:44.244773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:48.331162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:51.741071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:54.946986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:57.926600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.052344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:03.948979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:07.859855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:10.834857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:14.055119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:17.318335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:20.348263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:23.511601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:26.527284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:30.872090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:51.324121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:54.361929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:57.896544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:01.267470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:04.946837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:08.588309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:11.929296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:15.314927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:18.753603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:21.708984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:24.622027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:27.589377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:31.764085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:35.060575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:38.280930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:41.295447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:44.356742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:48.442378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:51.857315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.063191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.033827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.160691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.049324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:07.968169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:10.943205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:14.170424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:17.436668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:20.458189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:23.638806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:26.630619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:30.980316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:51.433395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:54.474236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.004868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:01.412637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:05.054574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:08.703582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.047493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:15.420159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:18.861923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:21.822295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:24.730247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:27.699593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:31.887270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:35.176779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:38.394141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:41.404669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:44.467953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:48.556589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:51.972003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.179905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.144077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.269012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.149666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:08.080488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:11.050529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:14.289714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:17.553531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:20.569512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:23.761006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:26.731965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:31.077570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:51.533676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:54.575240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.096235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:01.526914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:05.145334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:08.802863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.149734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:15.509442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:18.954261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:21.912603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:24.822509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:27.793850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:31.986552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:35.278059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:38.492395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:41.498932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:44.564204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:48.693298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:52.089205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.281148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.241300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.366333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.241029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:08.200742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:11.143886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:14.392052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:17.656864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:20.661750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:23.863856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:26.823332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:31.178908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:51.637889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:54.686254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.198537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:01.687587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:05.245580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:08.910058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.262948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:15.608689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:19.056605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:22.013946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:24.924744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:27.898504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:32.096738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:35.392266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:38.599620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:41.603167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:44.669432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:48.849427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:52.231341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.392498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.399013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.468698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.335392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:08.308065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:11.246227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:14.508254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:17.770183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:20.766124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:23.971209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:26.918686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:31.270177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:51.725290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:54.777300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:58:58.286883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:01.820746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:05.332859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:09.006315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:12.356212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:15.692977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:19.142985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:22.099311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:25.013054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:27.988775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:32.195986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:35.502524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:38.692884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:41.702415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:44.758702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:48.999098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:52.327660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:55.486729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T13:59:58.488291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:01.562057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:04.421746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:08.397368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:11.332617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:14.606596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:17.865509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:20.855492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:24.064478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-06T14:00:27.005062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-06T14:01:01.211930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AbsScoreDiffAccepted.PenaltyAirYardsAwayTimeouts_Remaining_PostAwayTimeouts_Remaining_PreAway_WP_postAway_WP_preDefTeamScoreDefensiveTeamDriveEPAExPoint_ProbExpPtsFieldGoalDistanceField_Goal_ProbFirstDownGoalToGoHomeTimeouts_Remaining_PostHomeTimeouts_Remaining_PreHome_WP_postHome_WP_preNo_Score_ProbOpp_Field_Goal_ProbOpp_Safety_ProbOpp_Touchdown_ProbPassAttemptPassLengthPassLocationPassOutcomePasserPasser_IDPenalty.YardsPlayTimeDiffPlayTypePosTeamScoreQBHitReceiverReceiver_IDReceptionRunGapRunLocationRushAttemptRusherRusher_IDSafety_ProbScoreDiffSideofFieldTimeSecsTimeUnderTimeout_IndicatorTouchdownTouchdown_ProbWPAWin_ProbYards.GainedYardsAfterCatchairEPAairWPAdownposteamposteam_timeouts_preqtrspyacEPAyacWPAydsnetydstogoyrdline100yrdln
AbsScoreDiff1.0000.0000.1640.3630.3630.2120.2270.7120.7750.7730.2300.9780.0001.0000.3210.0000.4490.7400.7400.2120.2270.5300.0000.0000.0000.0000.0000.2670.0000.6950.6760.0000.0000.4960.7870.0000.3920.4340.0000.0000.0000.0000.0000.3390.4100.9880.0000.4640.3291.0000.2050.1270.1680.6420.0800.0000.0000.0000.0000.7750.3890.7400.3840.0000.1260.2500.0000.2050.000
Accepted.Penalty0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.4720.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.9900.0000.9130.0000.0000.7671.0000.0001.0001.0000.0001.0001.0000.2820.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.000
AirYards0.1640.0001.0000.0000.0000.2990.1800.2290.0000.0690.0730.0000.1091.0000.1590.1580.5560.0260.026-0.299-0.1800.2370.0500.0070.0810.7750.8580.1770.0000.0000.0000.0000.0920.000-0.1860.4520.0720.1420.4921.0001.0000.3171.0001.0000.2170.2290.169-0.098-0.1370.0000.3780.0350.109-0.1150.2170.1940.6250.7080.0000.0000.0000.0260.000-0.458-0.4810.1890.307-0.022-0.047
AwayTimeouts_Remaining_Post0.3630.0000.0001.0000.9190.3540.2390.5470.0000.7920.0000.0000.0001.0000.4320.0000.0000.4100.4100.3540.2390.2650.2580.0000.0690.0000.0000.0000.0000.1300.2680.0000.0930.0000.6460.0000.6000.6140.0000.2720.0000.0000.0000.0000.3070.4740.4200.8750.8720.0000.0000.3600.0000.2190.0000.1420.0000.4060.0000.0000.7040.4100.0000.5890.4900.3570.3240.2670.356
AwayTimeouts_Remaining_Pre0.3630.0000.0000.9191.0000.3540.2390.5470.0000.8170.0000.0000.0001.0000.3490.0000.0000.4310.4310.3540.2390.2880.2500.0000.0000.0000.0000.0000.0000.1300.2680.0000.2540.0000.6460.0000.6000.6140.0000.2720.0000.0000.0000.0000.2340.4740.3830.8330.8400.0000.0000.3000.1520.2230.0000.0980.0000.4060.0000.0000.7350.4310.0000.5890.4900.3290.3260.3050.427
Away_WP_post0.2120.0000.2990.3540.3541.0000.8190.3040.3460.6690.0350.000-0.2381.000-0.1940.0000.5110.0000.000-1.000-0.8190.5330.3060.2220.3210.0000.2650.3130.3720.3910.4590.0000.1890.000-0.0170.0000.1910.2530.0740.0000.3860.0000.2370.0000.2710.4470.532-0.671-0.5791.0000.637-0.0870.052-0.2510.117-0.0160.1610.2900.0000.3460.2800.0000.331-0.023-0.068-0.3600.3640.3660.210
Away_WP_pre0.2270.0000.1800.2390.2390.8191.0000.2770.3870.626-0.0680.000-0.2161.000-0.2350.0000.6030.0000.000-0.819-1.0000.3300.2680.1690.3040.0000.2420.0000.0000.0000.0000.0000.1300.0000.0050.0000.0000.0330.0000.0000.0000.3000.0840.0000.2210.3570.402-0.647-0.4831.0000.257-0.026-0.062-0.174-0.045-0.1010.1730.2890.0000.3870.0000.0000.000-0.094-0.132-0.3510.3660.3370.136
DefTeamScore0.7120.0000.2290.5470.5470.3040.2771.0000.9780.9760.3260.3460.3071.0000.1520.0000.5300.7650.7650.3040.2770.4850.4040.4000.3850.0000.0000.1950.1460.9460.9430.1760.0000.1480.9450.0000.4980.4350.1000.2890.0000.0000.1380.4300.3770.7870.3660.6460.4751.0000.1420.3600.1960.6910.0000.0000.3840.1700.0000.9780.4950.7650.2310.4910.4180.5780.3970.4290.113
DefensiveTeam0.7750.0000.0000.0000.0000.3460.3870.9781.0000.9550.2220.0000.3991.0000.1570.0000.0650.0000.0000.3460.3870.2490.1590.3170.3250.0000.0000.3060.0000.8930.8840.0000.0000.0000.9660.0000.7670.7910.1090.0000.0000.0000.0000.9200.0000.9660.1680.5250.4531.0000.0000.3220.0000.9890.0000.0000.0000.0000.0000.9560.3370.0000.0000.4050.0000.5680.4160.5800.000
Drive0.7730.0000.0690.7920.8170.6690.6260.9760.9551.000-0.0520.220-0.0461.0000.0390.0000.5420.9570.957-0.669-0.6260.2400.5170.3830.5830.0000.0000.2470.2190.9180.9130.0980.2380.1030.2120.0000.5350.5020.1970.0000.0000.0000.2910.3200.5520.8380.374-0.970-0.0280.0000.2200.2530.0220.0240.0570.064-0.1270.0230.0000.9550.8070.9570.1060.0890.058-0.3870.4040.3280.178
EPA0.2300.0000.0730.0000.0000.035-0.0680.3260.222-0.0521.0000.000-0.1891.000-0.0650.5280.1070.1990.199-0.0350.068-0.0440.034-0.0000.0410.6140.3100.1590.7610.2180.0000.0000.0760.222-0.2110.0000.0000.0000.7900.0000.0000.0000.0000.000-0.0020.2840.2050.092-0.0240.0000.753-0.2370.911-0.2860.6530.571-0.086-0.0270.1950.2220.1170.1990.5430.6050.6000.165-0.1590.1150.329
ExPoint_Prob0.9780.0000.0000.0000.0000.0000.0000.3460.0000.2200.0001.0000.0001.0000.2040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0001.0000.0000.0000.9130.0000.0001.0001.0000.0001.0001.0000.0001.0001.0000.0000.9660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0000.0000.0000.1271.0001.0000.0000.0000.0000.000
ExpPts0.0000.0000.1090.0000.000-0.238-0.2160.3070.399-0.046-0.1890.0001.0001.0000.8020.3070.5310.0000.0000.2380.216-0.228-0.491-0.606-0.4260.0000.4750.0430.3720.4500.4240.0000.1510.325-0.3250.0000.0000.0000.2440.2890.3260.0000.2710.279-0.2390.0000.293-0.0370.2130.0000.0000.808-0.1490.2240.007-0.0570.0560.0570.2800.3990.0000.0000.000-0.263-0.2280.7120.005-0.739-0.373
FieldGoalDistance1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0001.0001.0001.0001.0001.0000.0000.0001.0000.0000.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.000
Field_Goal_Prob0.3210.4720.1590.4320.349-0.194-0.2350.1520.1570.039-0.0650.2040.8021.0001.0000.0000.3970.4890.4890.1940.235-0.173-0.361-0.401-0.2820.2790.3550.0000.0000.0000.0000.3710.0020.468-0.2980.1730.0000.0000.0000.0000.2670.2500.0000.673-0.0580.2580.077-0.1470.2780.2040.2550.483-0.0920.162-0.0130.0340.1400.1170.6250.1570.2930.4890.590-0.129-0.1260.6160.213-0.673-0.424
FirstDown0.0000.0000.1580.0000.0000.0000.0000.0000.0000.0000.5280.0000.3071.0000.0001.0000.1620.0000.0000.0000.0000.1210.5180.0000.3350.1960.0000.0000.3180.0000.0000.2140.0000.0000.0000.0000.3050.2950.3930.0000.0000.0000.0000.0000.3020.0000.0000.0000.0000.0000.0000.2900.6610.0000.5730.5180.0000.1190.0000.0000.0000.0000.0000.2190.7020.0000.2220.4070.537
GoalToGo0.4490.0450.5560.0000.0000.5110.6030.5300.0650.5420.1070.0000.5311.0000.3970.1621.0000.0000.0000.5110.6030.4730.5560.0000.4220.0000.0000.0000.0000.0000.0000.2570.0000.0000.5680.0000.2540.3060.0001.0000.5490.0000.0000.0000.4310.4750.1130.4780.4550.0000.0000.3410.0000.1460.0000.0000.0000.0000.1710.0650.0000.0000.0000.0000.5310.5610.3410.7230.786
HomeTimeouts_Remaining_Post0.7400.0000.0260.4100.4310.0000.0000.7650.0000.9570.1990.0000.0001.0000.4890.0000.0001.0000.9360.0000.0000.7620.1490.1680.3420.0000.0000.0000.0000.0000.0000.0000.1860.3260.6470.0000.0000.0000.0000.0000.0000.0000.0690.0690.8140.7330.0000.8530.5700.0000.0000.5010.0830.0890.0000.0000.0000.3760.0000.0000.5750.9360.0000.0000.0000.0000.0000.0000.000
HomeTimeouts_Remaining_Pre0.7400.0000.0260.4100.4310.0000.0000.7650.0000.9570.1990.0000.0001.0000.4890.0000.0000.9361.0000.0000.0000.7620.1490.1680.3420.0000.0000.0000.0000.0000.0000.0000.1860.3260.6470.0000.0000.0000.0000.0000.0000.0000.0690.0690.8140.7330.0000.8530.5700.0000.0000.5010.0830.0890.0000.0000.0000.3760.0000.0000.5750.9360.0000.0000.0000.0000.0000.0000.000
Home_WP_post0.2120.000-0.2990.3540.354-1.000-0.8190.3040.346-0.669-0.0350.0000.2381.0000.1940.0000.5110.0000.0001.0000.819-0.533-0.306-0.222-0.3210.0000.2650.3130.3720.3910.4590.000-0.1890.0000.0170.0000.1910.2530.0740.0000.3860.0000.2370.000-0.2710.4470.5320.6710.5791.0000.6370.087-0.0520.251-0.1170.016-0.161-0.2900.0000.3460.2800.0000.3310.0230.0680.360-0.364-0.366-0.210
Home_WP_pre0.2270.000-0.1800.2390.239-0.819-1.0000.2770.387-0.6260.0680.0000.2161.0000.2350.0000.6030.0000.0000.8191.000-0.330-0.268-0.169-0.3040.0000.2420.0000.0000.0000.0000.000-0.1300.000-0.0050.0000.0000.0330.0000.0000.0000.3000.0840.000-0.2210.3570.4020.6470.4831.0000.2570.0260.0620.1740.0450.101-0.173-0.2890.0000.3870.0000.0000.0000.0940.1320.351-0.366-0.337-0.136
No_Score_Prob0.5300.0000.2370.2650.2880.5330.3300.4850.2490.240-0.0440.000-0.2281.000-0.1730.1210.4730.7620.762-0.533-0.3301.0000.4110.4670.3470.2460.2460.0000.0000.0520.0000.000-0.0000.000-0.0470.0000.0000.0000.1500.0000.0000.1780.0000.1530.2280.4470.000-0.186-0.5620.0000.390-0.0910.156-0.1510.2170.0530.4380.4900.0000.2490.4330.7620.033-0.085-0.121-0.3890.3380.4570.200
Opp_Field_Goal_Prob0.0000.0000.0500.2580.2500.3060.2680.4040.1590.5170.0340.000-0.4911.000-0.3610.5180.5560.1490.149-0.306-0.2680.4111.0000.9350.9870.0000.3750.1740.0000.0000.0620.147-0.0290.2090.4050.0000.0000.0000.2380.0000.3670.0000.3250.0000.8630.0000.000-0.3500.2790.0000.000-0.0220.1870.1390.2180.292-0.292-0.2590.2050.1590.2680.1490.0000.3370.314-0.7580.3430.8160.522
Opp_Safety_Prob0.0000.0000.0070.0000.0000.2220.1690.4000.3170.383-0.0000.000-0.6061.000-0.4010.0000.0000.1680.168-0.222-0.1690.4670.9351.0000.8900.1830.3750.0000.0620.3050.2330.000-0.1570.0000.4810.0000.0000.0000.3320.6710.2870.3410.4220.0000.7300.0000.068-0.2360.2010.0000.000-0.2280.1330.1830.1760.220-0.227-0.1940.0000.3170.2000.1680.0000.2870.275-0.7870.2810.8150.445
Opp_Touchdown_Prob0.0000.0000.0810.0690.0000.3210.3040.3850.3250.5830.0410.000-0.4261.000-0.2820.3350.4220.3420.342-0.321-0.3040.3470.9870.8901.0000.2760.4390.0000.2950.1060.0000.000-0.0160.0920.3570.1890.0000.0000.3740.2890.3340.0000.3370.3480.8960.0000.000-0.4250.3210.0000.0000.0350.1740.1080.1900.295-0.292-0.2590.0710.3250.3330.3420.0000.3370.314-0.7090.3830.7630.494
PassAttempt0.0000.0000.7750.0000.0000.0000.0000.0000.0000.0000.6140.0000.0001.0000.2790.1960.0000.0000.0000.0000.0000.2460.0000.1830.2761.0001.0001.0001.0001.0001.0000.1780.0000.8780.0000.0001.0001.0000.6021.0001.0000.4981.0001.0000.1840.0000.0000.0000.0000.0000.0000.2930.5280.0000.4610.4041.0001.0000.1890.0000.0000.0000.0001.0001.0000.0000.2300.0000.000
PassLength0.0000.0000.8580.0000.0000.2650.2420.0000.0000.0000.3101.0000.4750.0000.3550.0000.0000.0000.0000.2650.2420.2460.3750.3750.4391.0001.0000.3730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.4050.0000.0000.0760.0941.0000.0000.3740.0000.0000.2930.0000.1280.3080.0000.0000.0000.0000.0000.5570.4300.0000.0000.0000.385
PassLocation0.2670.0000.1770.0000.0000.3130.0000.1950.3060.2470.1591.0000.0430.0000.0000.0000.0000.0000.0000.3130.0000.0000.1740.0000.0001.0000.3731.0000.2680.3060.2490.0000.0000.0000.2470.0000.0400.0000.2680.0000.0001.0000.0000.0000.2010.1560.4730.0000.0001.0000.0000.0000.0000.1200.0000.0000.3400.5780.0000.3060.0000.0000.0000.0000.0000.0000.0000.0000.224
PassOutcome0.0000.0000.0000.0000.0000.3720.0000.1460.0000.2190.7611.0000.3720.0000.0000.3180.0000.0000.0000.3720.0000.0000.0000.0620.2951.0000.0000.2681.0000.0000.0000.0000.0000.0000.2190.0000.0000.0000.8970.0000.0001.0000.0000.0000.0000.1540.0000.0000.1381.0000.0000.3120.7950.2060.7950.4730.0000.2810.0000.0000.0000.0000.0000.8160.8160.0000.4650.1860.361
Passer0.6950.0000.0000.1300.1300.3910.0000.9460.8930.9180.2181.0000.4500.0000.0000.0000.0000.0000.0000.3910.0000.0520.0000.3050.1061.0000.0000.3060.0001.0000.8840.0000.0000.0000.9180.0000.7670.7910.0000.0000.0001.0000.0000.0000.0000.9460.0000.4350.4731.0000.0000.0000.2990.9730.0000.0000.0000.0000.0000.8930.4750.0000.0000.4050.0000.2860.4420.3920.000
Passer_ID0.6761.0000.0000.2680.2680.4590.0000.9430.8840.9130.0001.0000.4240.0000.0000.0000.0000.0000.0000.4590.0000.0000.0620.2330.0001.0000.0000.2490.0000.8841.0001.0000.0001.0000.9130.0000.7910.7910.0000.0000.0001.0000.0000.0000.0000.9430.0000.5340.5651.0000.0000.0000.1400.9720.0000.0000.0000.0000.0000.8840.4470.0000.0000.4050.0000.0970.4850.3290.000
Penalty.Yards0.0000.9900.0000.0000.0000.0000.0000.1760.0000.0980.0000.0000.0001.0000.3710.2140.2570.0000.0000.0000.0000.0000.1470.0000.0000.1780.0000.0000.0000.0001.0001.0000.0000.5740.1010.0000.7671.0000.0001.0001.0000.0001.0001.0000.1930.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.000
PlayTimeDiff0.0000.0000.0920.0930.2540.1890.1300.0000.0000.2380.0760.0000.1511.0000.0020.0000.0000.1860.186-0.189-0.130-0.000-0.029-0.157-0.0160.0000.0000.0000.0000.0000.0000.0001.0000.161-0.0760.0000.0000.0000.1010.0000.4140.2430.0000.0000.0530.0000.000-0.247-0.0180.4250.0000.3120.164-0.1040.1610.097-0.152-0.0990.1800.0000.2830.1860.0000.1460.1470.1250.0340.0180.054
PlayType0.4960.9130.0000.0000.0000.0000.0000.1480.0000.1030.2220.9130.3251.0000.4680.0000.0000.3260.3260.0000.0000.0000.2090.0000.0920.8780.0000.0000.0000.0001.0000.5740.1611.0000.0000.0000.7671.0000.5441.0001.0000.9131.0001.0000.1750.4160.0000.0000.0000.9130.0000.2260.0000.0000.0000.0001.0001.0000.5550.0000.0000.3260.6521.0001.0000.0000.1210.2180.270
PosTeamScore0.7870.000-0.1860.6460.646-0.0170.0050.9450.9660.212-0.2110.000-0.3251.000-0.2980.0000.5680.6470.6470.017-0.005-0.0470.4050.4810.3570.0000.0000.2470.2190.9180.9130.101-0.0760.0001.0000.0000.5350.5020.2390.1830.0000.0000.1280.3370.2930.8520.339-0.2040.2131.0000.325-0.154-0.1160.845-0.139-0.113-0.111-0.1230.0000.9660.6120.6470.000-0.0620.005-0.4610.0930.334-0.035
QBHit0.0000.0000.4520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.1730.0000.0000.0000.0000.0000.0000.0000.0000.0000.1890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0001.0000.0001.0001.0000.0000.0000.0000.3200.0000.0000.0000.0000.0000.0000.0000.5970.1570.5120.3360.0000.0000.0000.0000.4760.2830.0000.1280.0000.334
Receiver0.3920.7670.0720.6000.6000.1910.0000.4980.7670.5350.0001.0000.0000.0000.0000.3050.2540.0000.0000.1910.0000.0000.0000.0000.0001.0000.0000.0400.0000.7670.7910.7670.0000.7670.5350.0001.0001.0000.0000.0000.0001.0000.0000.0000.0630.5160.0000.0000.3031.0000.0000.3230.0000.4730.0000.0000.0780.0000.0000.7670.6780.0000.0000.0000.0000.0000.0000.0000.000
Receiver_ID0.4341.0000.1420.6140.6140.2530.0330.4350.7910.5020.0001.0000.0000.0000.0000.2950.3060.0000.0000.2530.0330.0000.0000.0000.0001.0000.0000.0000.0000.7910.7911.0000.0001.0000.5020.0001.0001.0000.0000.0000.0001.0000.0000.0000.1990.5560.0000.0000.2651.0000.1650.3580.1060.5120.0000.0000.0780.0000.0000.7910.7020.0000.1650.0000.0000.0000.0000.0000.000
Reception0.0000.0000.4920.0000.0000.0740.0000.1000.1090.1970.7900.0000.2441.0000.0000.3930.0000.0000.0000.0740.0000.1500.2380.3320.3740.6020.0000.2680.8970.0000.0000.0000.1010.5440.2390.0000.0000.0001.0001.0001.0000.2861.0001.0000.0170.2110.0000.0000.0000.0000.2000.4540.8280.1240.8180.7510.0000.2810.0000.1090.0000.0000.0000.8160.8160.0000.3300.1200.186
RunGap0.0001.0001.0000.2720.2720.0000.0000.2890.0000.0000.0001.0000.2890.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.6710.2891.0000.0000.0000.0000.0000.0001.0000.0001.0000.1831.0000.0000.0001.0001.0000.0001.0000.5770.1830.0000.0000.0000.3540.0001.0001.0000.2890.0000.0000.1261.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.4470.0000.433
RunLocation0.0001.0001.0000.0000.0000.3860.0000.0000.0000.0000.0001.0000.3260.0000.2670.0000.5490.0000.0000.3860.0000.0000.3670.2870.3341.0000.0000.0000.0000.0000.0001.0000.4141.0000.0001.0000.0000.0001.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.2021.0001.0000.0700.3220.0000.0001.0000.0000.0000.2080.0000.0000.0001.0000.0000.0000.0000.0000.3980.352
RushAttempt0.0000.0000.3170.0000.0000.0000.3000.0000.0000.0000.0000.0000.0001.0000.2500.0000.0000.0000.0000.0000.3000.1780.0000.3410.0000.4981.0001.0001.0001.0001.0000.0000.2430.9130.0000.0001.0001.0000.2861.0001.0001.0001.0001.0000.1860.0000.0000.0000.0000.0000.0000.2710.0000.0000.6080.0001.0001.0000.3090.0000.0000.0000.0281.0001.0000.1530.1450.1290.099
Rusher0.0001.0001.0000.0000.0000.2370.0840.1380.0000.2910.0001.0000.2710.0000.0000.0000.0000.0690.0690.2370.0840.0000.3250.4220.3371.0000.0000.0000.0000.0000.0001.0000.0001.0000.1281.0000.0000.0001.0000.5770.0001.0001.0000.1790.3020.0000.1530.4650.0001.0001.0000.4700.0000.1100.0001.0000.0000.0000.0000.0000.0000.0691.0000.0000.0000.0000.3400.0000.412
Rusher_ID0.3391.0001.0000.0000.0000.0000.0000.4300.9200.3200.0001.0000.2790.0000.6730.0000.0000.0690.0690.0000.0000.1530.0000.0000.3481.0000.0000.0000.0000.0000.0001.0000.0001.0000.3371.0000.0000.0001.0000.1830.0001.0000.1791.0000.0000.4220.0000.3310.5671.0001.0000.1880.4230.5950.2811.0000.0000.0000.0000.9200.0000.0691.0000.0000.0000.5440.0000.0000.044
Safety_Prob0.4100.2820.2170.3070.2340.2710.2210.3770.0000.552-0.0020.000-0.2391.000-0.0580.3020.4310.8140.814-0.271-0.2210.2280.8630.7300.8960.1840.4050.2010.0000.0000.0000.1930.0530.1750.2930.0000.0630.1990.0170.0000.0000.1860.3020.0001.0000.3930.178-0.4360.4060.0000.0000.1230.1260.1250.1460.309-0.128-0.1070.3430.0000.5320.8140.3610.1270.109-0.5230.4170.5500.323
ScoreDiff0.9880.0000.2290.4740.4740.4470.3570.7870.9660.8380.2840.9660.0001.0000.2580.0000.4750.7330.7330.4470.3570.4470.0000.0000.0000.0000.0000.1560.1540.9460.9430.0000.0000.4160.8520.0000.5160.5560.2110.0000.0000.0000.0000.4220.3931.0000.0650.5670.3211.0000.3250.1700.2420.9120.2400.0000.0000.0000.0000.9660.6390.7330.3830.1220.0000.3490.0000.2530.052
SideofField0.0000.0000.1690.4200.3830.5320.4020.3660.1680.3740.2050.0000.2931.0000.0770.0000.1130.0000.0000.5320.4020.0000.0000.0680.0000.0000.0000.4730.0000.0000.0000.0000.0000.0000.3390.0000.0000.0000.0000.0000.0000.0000.1530.0000.1780.0651.0000.5370.5330.0000.0000.1740.2400.2560.0630.0000.3770.3400.0000.1680.2560.0000.0000.0000.0000.4660.3950.5060.554
TimeSecs0.4640.034-0.0980.8750.833-0.671-0.6470.6460.525-0.9700.0920.000-0.0371.000-0.1470.0000.4780.8530.8530.6710.647-0.186-0.350-0.236-0.4250.0000.0760.0000.0000.4350.5340.000-0.2470.000-0.2040.3200.0000.0000.0000.3540.0000.0000.4650.331-0.4360.5670.5371.0000.0990.0000.000-0.2230.045-0.0470.0260.0070.005-0.1350.0000.5250.7230.8530.0000.0380.0590.245-0.441-0.176-0.011
TimeUnder0.3290.000-0.1370.8720.840-0.579-0.4830.4750.453-0.028-0.0240.0000.2131.0000.2780.0000.4550.5700.5700.5790.483-0.5620.2790.2010.3210.0000.0940.0000.1380.4730.5650.000-0.0180.0000.2130.0000.3030.2650.0000.0000.2020.0000.0000.5670.4060.3210.5330.0991.0000.0000.0000.341-0.0020.3970.0350.194-0.469-0.5500.0000.4530.7030.5700.0000.2160.2340.067-0.033-0.0730.039
Timeout_Indicator1.0000.0000.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0001.0000.2040.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0001.0001.0001.0001.0001.0000.0000.4250.9131.0000.0001.0001.0000.0001.0001.0000.0001.0001.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.0000.0000.0001.0001.0000.0000.0000.0000.373
Touchdown0.2050.0000.3780.0000.0000.6370.2570.1420.0000.2200.7530.0000.0001.0000.2550.0000.0000.0000.0000.6370.2570.3900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3250.0000.0000.1650.2001.0001.0000.0001.0001.0000.0000.3250.0000.0000.0000.0001.0000.2830.7620.2650.3180.0000.0000.0000.0000.0000.0620.0000.4230.2830.8160.0000.0000.0000.000
Touchdown_Prob0.1270.0000.0350.3600.300-0.087-0.0260.3600.3220.253-0.2370.0000.8081.0000.4830.2900.3410.5010.5010.0870.026-0.091-0.022-0.2280.0350.2930.3740.0000.3120.0000.0000.0000.3120.226-0.1540.0000.3230.3580.4540.2890.0700.2710.4700.1880.1230.1700.174-0.2230.3410.0000.2831.000-0.0750.2720.1140.042-0.406-0.3430.3890.3220.2890.5010.4140.0830.1130.2890.035-0.309-0.015
WPA0.1680.0000.1090.0000.1520.052-0.0620.1960.0000.0220.9110.000-0.1491.000-0.0920.6610.0000.0830.083-0.0520.0620.1560.1870.1330.1740.5280.0000.0000.7950.2990.1400.0000.1640.000-0.1160.0000.0000.1060.8280.0000.3220.0000.0000.4230.1260.2420.2400.045-0.0020.0000.762-0.0751.000-0.2020.7150.577-0.098-0.0320.0000.0000.2850.0830.3730.6040.6120.038-0.1040.2140.351
Win_Prob0.6420.000-0.1150.2190.223-0.251-0.1740.6910.9890.024-0.2860.0000.2241.0000.1620.0000.1460.0890.0890.2510.174-0.1510.1390.1830.1080.0000.0000.1200.2060.9730.9720.000-0.1040.0000.8450.0000.4730.5120.1240.0000.0000.0000.1100.5950.1250.9120.256-0.0470.3970.0000.2650.272-0.2021.000-0.048-0.084-0.138-0.1590.0000.9890.4640.0890.000-0.106-0.029-0.0080.043-0.060-0.202
Yards.Gained0.0800.0000.2170.0000.0000.117-0.0450.0000.0000.0570.6530.0000.0071.000-0.0130.5730.0000.0000.000-0.1170.0450.2170.2180.1760.1900.4610.2930.0000.7950.0000.0000.0000.1610.000-0.1390.0000.0000.0000.8180.1260.0000.6080.0000.2810.1460.2400.0630.0260.0350.0000.3180.1140.715-0.0481.0000.674-0.262-0.2020.0000.0000.0000.0000.0000.7180.7270.0750.0160.2570.458
YardsAfterCatch0.0000.0000.1940.1420.098-0.016-0.1010.0000.0000.0640.5710.000-0.0571.0000.0340.5180.0000.0000.0000.0160.1010.0530.2920.2200.2950.4040.0000.0000.4730.0000.0000.0000.0970.000-0.1130.5970.0000.0000.7511.0001.0000.0001.0001.0000.3090.0000.0000.0070.1940.0000.0000.0420.577-0.0840.6741.000-0.664-0.6650.0000.0000.0000.0000.0000.8800.8840.0490.0940.2610.432
airEPA0.0001.0000.6250.0000.0000.1610.1730.3840.000-0.127-0.0861.0000.0560.0000.1400.0000.0000.0000.000-0.161-0.1730.438-0.292-0.227-0.2921.0000.1280.3400.0000.0000.0001.000-0.1521.000-0.1110.1570.0780.0780.0000.0000.0001.0000.0000.000-0.1280.0000.3770.005-0.4691.0000.000-0.406-0.098-0.138-0.262-0.6641.0000.9640.2500.0000.0000.0000.000-0.792-0.7780.2340.256-0.254-0.578
airWPA0.0001.0000.7080.4060.4060.2900.2890.1700.0000.023-0.0271.0000.0570.0000.1170.1190.0000.3760.376-0.290-0.2890.490-0.259-0.194-0.2591.0000.3080.5780.2810.0000.0001.000-0.0991.000-0.1230.5120.0000.0000.2810.0000.0001.0000.0000.000-0.1070.0000.340-0.135-0.5501.0000.000-0.343-0.032-0.159-0.202-0.6650.9641.0000.0000.0000.2060.3760.000-0.764-0.7660.2010.276-0.206-0.495
down0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1951.0000.2801.0000.6250.0000.1710.0000.0000.0000.0000.0000.2050.0000.0710.1890.0000.0000.0000.0000.0000.0000.1800.5550.0000.3360.0000.0000.0000.0000.2080.3090.0000.0000.3430.0000.0000.0000.0001.0000.0000.3890.0000.0000.0000.0000.2500.0001.0000.0000.0000.0000.4960.0000.0000.0000.3770.0000.000
posteam0.7750.0000.0000.0000.0000.3460.3870.9780.9560.9550.2220.0000.3991.0000.1570.0000.0650.0000.0000.3460.3870.2490.1590.3170.3250.0000.0000.3060.0000.8930.8840.0000.0000.0000.9660.0000.7670.7910.1090.0000.0000.0000.0000.9200.0000.9660.1680.5250.4531.0000.0000.3220.0000.9890.0000.0000.0000.0000.0001.0000.3370.0000.0000.4050.0000.5680.4160.5800.000
posteam_timeouts_pre0.3890.0000.0000.7040.7350.2800.0000.4950.3370.8070.1170.0000.0001.0000.2930.0000.0000.5750.5750.2800.0000.4330.2680.2000.3330.0000.0000.0000.0000.4750.4470.0000.2830.0000.6120.0000.6780.7020.0000.0000.0000.0000.0000.0000.5320.6390.2560.7230.7030.0000.0620.2890.2850.4640.0000.0000.0000.2060.0000.3371.0000.5750.1690.5800.4760.2530.3130.2370.137
qtr0.7400.0000.0260.4100.4310.0000.0000.7650.0000.9570.1990.0000.0001.0000.4890.0000.0000.9360.9360.0000.0000.7620.1490.1680.3420.0000.0000.0000.0000.0000.0000.0000.1860.3260.6470.0000.0000.0000.0000.0000.0000.0000.0690.0690.8140.7330.0000.8530.5700.0000.0000.5010.0830.0890.0000.0000.0000.3760.0000.0000.5751.0000.0000.0000.0000.0000.0000.0000.000
sp0.3840.0000.0000.0000.0000.3310.0000.2310.0000.1060.5430.1270.0001.0000.5900.0000.0000.0000.0000.3310.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6520.0000.0000.0000.1650.0001.0001.0000.0281.0001.0000.3610.3830.0000.0000.0000.0000.4230.4140.3730.0000.0000.0000.0000.0000.4960.0000.1690.0001.0000.2830.8160.1730.0000.4280.000
yacEPA0.0001.000-0.4580.5890.589-0.023-0.0940.4910.4050.0890.6051.000-0.2630.000-0.1290.2190.0000.0000.0000.0230.094-0.0850.3370.2870.3371.0000.5570.0000.8160.4050.4051.0000.1461.000-0.0620.4760.0000.0000.8160.0000.0001.0000.0000.0000.1270.1220.0000.0380.2161.0000.2830.0830.604-0.1060.7180.880-0.792-0.7640.0000.4050.5800.0000.2831.0000.988-0.133-0.2490.3390.712
yacWPA0.1261.000-0.4810.4900.490-0.068-0.1320.4180.0000.0580.6001.000-0.2280.000-0.1260.7020.5310.0000.0000.0680.132-0.1210.3140.2750.3141.0000.4300.0000.8160.0000.0001.0000.1471.0000.0050.2830.0000.0000.8160.0000.0001.0000.0000.0000.1090.0000.0000.0590.2341.0000.8160.1130.612-0.0290.7270.884-0.778-0.7660.0000.0000.4760.0000.8160.9881.000-0.094-0.3260.3040.692
ydsnet0.2500.0000.1890.3570.329-0.360-0.3510.5780.568-0.3870.1650.0000.7121.0000.6160.0000.5610.0000.0000.3600.351-0.389-0.758-0.787-0.7090.0000.0000.0000.0000.2860.0970.0000.1250.000-0.4610.0000.0000.0000.0000.0000.0000.1530.0000.544-0.5230.3490.4660.2450.0670.0000.0000.2890.038-0.0080.0750.0490.2340.2010.0000.5680.2530.0000.173-0.133-0.0941.000-0.151-0.815-0.501
ydstogo0.0000.0000.3070.3240.3260.3640.3660.3970.4160.404-0.1590.0000.0051.0000.2130.2220.3410.0000.000-0.364-0.3660.3380.3430.2810.3830.2300.0000.0000.4650.4420.4850.0000.0340.1210.0930.1280.0000.0000.3300.4470.0000.1450.3400.0000.4170.0000.395-0.441-0.0330.0000.0000.035-0.1040.0430.0160.0940.2560.2760.3770.4160.3130.0000.000-0.249-0.326-0.1511.0000.319-0.125
yrdline1000.2050.000-0.0220.2670.3050.3660.3370.4290.5800.3280.1150.000-0.7391.000-0.6730.4070.7230.0000.000-0.366-0.3370.4570.8160.8150.7630.0000.0000.0000.1860.3920.3290.0000.0180.2180.3340.0000.0000.0000.1200.0000.3980.1290.0000.0000.5500.2530.506-0.176-0.0730.0000.000-0.3090.214-0.0600.2570.261-0.254-0.2060.0000.5800.2370.0000.4280.3390.304-0.8150.3191.0000.619
yrdln0.0000.000-0.0470.3560.4270.2100.1360.1130.0000.1780.3290.000-0.3731.000-0.4240.5370.7860.0000.000-0.210-0.1360.2000.5220.4450.4940.0000.3850.2240.3610.0000.0000.0000.0540.270-0.0350.3340.0000.0000.1860.4330.3520.0990.4120.0440.3230.0520.554-0.0110.0390.3730.000-0.0150.351-0.2020.4580.432-0.578-0.4950.0000.0000.1370.0000.0000.7120.692-0.501-0.1250.6191.000

Missing values

2024-09-06T14:00:31.641568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-06T14:00:32.295066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-06T14:00:33.433098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GameIDDriveqtrdownTimeUnderTimeSecsPlayTimeDiffSideofFieldyrdlnyrdline100ydstogoydsnetGoalToGoFirstDownposteamDefensiveTeamdescPlayAttemptedYards.GainedspTouchdownExPointResultTwoPointConvDefTwoPointSafetyOnsidekickPuntResultPlayTypePasserPasser_IDPassAttemptPassOutcomePassLengthAirYardsYardsAfterCatchQBHitPassLocationInterceptionThrownInterceptorRusherRusher_IDRushAttemptRunLocationRunGapReceiverReceiver_IDReceptionReturnResultReturnerBlockingPlayerTackler1Tackler2FieldGoalResultFieldGoalDistanceFumbleRecFumbTeamRecFumbPlayerSackChallenge.ReplayChalReplayResultAccepted.PenaltyPenalizedTeamPenaltyTypePenalizedPlayerPenalty.YardsPosTeamScoreDefTeamScoreScoreDiffAbsScoreDiffHomeTeamAwayTeamTimeout_IndicatorTimeout_Teamposteam_timeouts_preHomeTimeouts_Remaining_PreAwayTimeouts_Remaining_PreHomeTimeouts_Remaining_PostAwayTimeouts_Remaining_PostNo_Score_ProbOpp_Field_Goal_ProbOpp_Safety_ProbOpp_Touchdown_ProbField_Goal_ProbSafety_ProbTouchdown_ProbExPoint_ProbTwoPoint_ProbExpPtsEPAairEPAyacEPAHome_WP_preAway_WP_preHome_WP_postAway_WP_postWin_ProbWPAairWPAyacWPASeason
02016121101132NaN21920.05.0SD44.044.00220.0NaNNaNNaNTwo-Minute Warning1000NaNNaNNaN00NaNTwo Minute WarningNaNNaN0NaNNaN000NaN0NaNNaNNaN0NaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN0NaNNaNNaNNaNCARSD0NaN222220.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000001.829564NaNNaNNaNNaNNaNNaN0.000000NaNNaNNaN2016
120161211011321.021920.00.0SD47.053.010680.01.0SDCAR(2:00) (Shotgun) P.Rivers pass deep left to Ty.Williams to CAR 7 for 46 yards (D.Worley). Penalty on CAR-K.Short, Defensive Offside, declined.14600NaNNaNNaN00NaNPassP.Rivers00-00229421CompleteDeep4330left0NaNNaNNaN0NaNNaNTy.Williams00-00321601NaNNaNNaND.WorleyNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN00.023.0-23.023.0CARSD0NaN222220.4093910.0509490.0003590.0744800.1849330.0019050.2779830.0000000.01.8295642.6414362.3109610.3304750.9680250.0319750.9401700.0598300.0319750.0278550.0242990.0035562016
220161211011321.021910.010.0CAR7.07.07661.00.0SDCAR(1:50) (Shotgun) K.Farrow left guard to CAR 9 for -2 yards (V.Butler).1-200NaNNaNNaN00NaNRunNaNNaN0NaNNaN000NaN0NaNK.Farrow00-00329021middleNaNNaNNaN0NaNNaNNaNV.ButlerNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN00.023.0-23.023.0CARSD0NaN222220.1274230.0079400.0000100.0044340.3732270.0005130.4864530.0000000.04.471000-0.660184NaNNaN0.9401700.0598300.9475750.0524250.059830-0.007405NaNNaN2016
320161211011322.021877.033.0CAR9.09.09661.00.0SDCAR(1:17) (No Huddle, Shotgun) P.Rivers pass incomplete short right.1000NaNNaNNaN00NaNPassP.Rivers00-00229421Incomplete PassShort900right0NaNNaNNaN0NaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN00.023.0-23.023.0CARSD0NaN222220.1591280.0090980.0000210.0050890.4776380.0004650.3485610.0000000.03.810815-0.5769753.189185-3.7661590.9475750.0524250.9536010.0463990.052425-0.0060250.036136-0.0421612016
420161211011323.021868.09.0CAR9.09.09751.00.0SDCAR(1:08) (Shotgun) P.Rivers pass short left to H.Henry for 9 yards, TOUCHDOWN.1911NaNNaNNaN00NaNPassP.Rivers00-00229421CompleteShort900left0NaNNaNNaN0NaNNaNH.Henry00-00330901NaNNaNNaNNaNNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN00.023.0-23.023.0CARSD0NaN222220.1746730.0100350.0000330.0059430.5891650.0005880.2195640.0000000.03.2338413.7661593.7661590.0000000.9536010.0463990.9123540.0876460.0463990.0412470.0412470.0000002016
52016121101132NaN21863.05.0CAR15.015.00750.00.0SDCARJ.Lambo extra point is GOOD, Center-M.Windt, Holder-K.Clemens.1010MadeNaNNaN00NaNExtra PointNaNNaN0NaNNaN000NaN0NaNNaNNaN0NaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN06.023.0-17.017.0CARSD0NaN222220.0000000.0000000.0000000.0000000.0000000.0000000.0000000.9311150.00.9311150.068885NaNNaN0.9123540.0876460.9270970.0729030.087646-0.014743NaNNaN2016
62016121101142NaN21863.00.0SD35.035.0000.0NaNCARSDJ.Lambo kicks 66 yards from SD 35 to CAR -1. J.Webb to CAR 20 for 21 yards (D.Watt).12100NaNNaNNaN00NaNKickoffNaNNaN0NaNNaN000NaN0NaNNaNNaN0NaNNaNNaNNaN0NaNJ.WebbNaND.WattNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN023.07.016.016.0CARSD0NaN222220.6814330.0451130.0023260.0327180.1257520.0004850.1121740.0000000.00.794425-0.177811NaNNaN0.9270970.0729030.9250510.0749490.927097-0.002047NaNNaN2016
720161211011421.011858.05.0CAR20.080.01050.00.0CARSD(:58) J.Stewart left guard to CAR 25 for 5 yards (T.Palepoi; J.Brown).1500NaNNaNNaN00NaNRunNaNNaN0NaNNaN000NaN0NaNK.Farrow00-00261531righttackleNaNNaN0NaNNaNNaNT.PalepoiJ.BrownNaNNaN0NaNNaN00NaN0NaNNaNNaN023.07.016.016.0CARSD0NaN222220.7118550.0465450.0032460.0337760.1076630.0004450.0964700.0000000.00.616614-0.015020NaNNaN0.9250510.0749490.9252170.0747830.9250510.000167NaNNaN2016
820161211011422.011825.033.0CAR25.075.0550.00.0CARSD(:25) (Shotgun) J.Stewart up the middle to CAR 25 for no gain (T.Palepoi).1000NaNNaNNaN00NaNRunNaNNaN0NaNNaN000NaN0NaNK.Farrow00-00261531leftguardNaNNaN0NaNNaNNaNT.PalepoiNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN023.07.016.016.0CARSD0NaN222220.7284360.0438440.0022300.0313580.0998780.0004630.0937900.0000000.00.601594-0.601594NaNNaN0.9252170.074783NaNNaN0.925217NaNNaNNaN2016
92016121101142NaN01800.025.0CAR25.025.0050.00.0NaNNaNEND QUARTER 21000NaNNaNNaN00NaNQuarter EndNaNNaN0NaNNaN000NaN0NaNNaNNaN0NaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN0NaNNaNNaNNaNCARSD0NaN222220.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.000000NaNNaNNaNNaNNaNNaN0.0000000.000000NaNNaN2016
GameIDDriveqtrdownTimeUnderTimeSecsPlayTimeDiffSideofFieldyrdlnyrdline100ydstogoydsnetGoalToGoFirstDownposteamDefensiveTeamdescPlayAttemptedYards.GainedspTouchdownExPointResultTwoPointConvDefTwoPointSafetyOnsidekickPuntResultPlayTypePasserPasser_IDPassAttemptPassOutcomePassLengthAirYardsYardsAfterCatchQBHitPassLocationInterceptionThrownInterceptorRusherRusher_IDRushAttemptRunLocationRunGapReceiverReceiver_IDReceptionReturnResultReturnerBlockingPlayerTackler1Tackler2FieldGoalResultFieldGoalDistanceFumbleRecFumbTeamRecFumbPlayerSackChallenge.ReplayChalReplayResultAccepted.PenaltyPenalizedTeamPenaltyTypePenalizedPlayerPenalty.YardsPosTeamScoreDefTeamScoreScoreDiffAbsScoreDiffHomeTeamAwayTeamTimeout_IndicatorTimeout_Teamposteam_timeouts_preHomeTimeouts_Remaining_PreAwayTimeouts_Remaining_PreHomeTimeouts_Remaining_PostAwayTimeouts_Remaining_PostNo_Score_ProbOpp_Field_Goal_ProbOpp_Safety_ProbOpp_Touchdown_ProbField_Goal_ProbSafety_ProbTouchdown_ProbExPoint_ProbTwoPoint_ProbExpPtsEPAairEPAyacEPAHome_WP_preAway_WP_preHome_WP_postAway_WP_postWin_ProbWPAairWPAyacWPASeason
4020161211011731.051166.00.0CAR25.075.010-100.01.0CARSD(4:26) (Shotgun) C.Newton pass incomplete short right to J.Stewart. PENALTY on CAR-E.Dickson, Offensive Pass Interference, 10 yards, enforced at CAR 25 - No Play.1000NaNNaNNaN00NaNNo PlayC.NewtonNaN1Incomplete PassShort000right0NaNNaNNaN0NaNNaNJ.StewartNaN0NaNNaNNaNNaNNaNNaNNaN0NaNNaN00NaN1CARNaNE.Dickson1026.010.016.016.0CARSD0NaN332320.0173330.1562860.0042280.2384680.2332150.0035020.3469680.00.00.988837-1.340816NaNNaN0.9323770.0676230.9161820.0838180.932377-0.016194NaNNaN2016
4120161211011731.051160.06.0CAR15.085.020-50.00.0CARSD(4:20) (Shotgun) C.Newton up the middle to CAR 20 for 5 yards (C.Liuget).1500NaNNaNNaN00NaNRunNaNNaN0NaNNaN000NaN0NaNT.Benjamin00-00279391leftendNaNNaN0NaNNaNNaNC.LiugetNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN026.010.016.016.0CARSD0NaN332320.0203920.1995790.0100740.3047360.2093260.0038370.2520580.00.0-0.351978-0.095141NaNNaN0.9161820.0838180.9157000.0843000.916182-0.000482NaNNaN2016
4220161211011732.041121.039.0CAR20.080.015-80.00.0CARSD(3:41) (Shotgun) J.Stewart left tackle to CAR 17 for -3 yards (K.Emanuel; J.Addae).1-300NaNNaNNaN00NaNRunNaNNaN0NaNNaN000NaN0NaNK.Farrow00-00261531righttackleNaNNaN0NaNNaNNaNK.EmanuelJ.AddaeNaNNaN0NaNNaN00NaN0NaNNaNNaN026.010.016.016.0CARSD0NaN332320.0227760.2037990.0083020.3135960.1914970.0037320.2562990.00.0-0.447120-1.325485NaNNaN0.9157000.0843000.8990700.1009300.915700-0.016630NaNNaN2016
4320161211011733.031078.043.0CAR17.083.018-80.00.0CARSD(2:58) (Shotgun) C.Newton pass incomplete deep right to C.Brown. Penalty on CAR-M.Remmers, Offensive Holding, declined.1000NaNNaNNaN00NaNPassC.Newton00-00279391Incomplete PassDeep4300right0NaNNaNNaN0NaNNaNC.Brown00-00311180NaNNaNNaNNaNNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN026.010.016.016.0CARSD0NaN332320.0265340.2550860.0104940.3850120.1323810.0043740.1861200.00.0-1.772605-1.0397795.411689-6.4514680.8990700.1009300.8839680.1160320.899070-0.0151020.063150-0.0782522016
4420161211011734.031068.010.0CAR17.083.018-80.01.0CARSD(2:48) M.Palardy punts 48 yards to SD 35, Center-J.Jansen. I.Burse to SD 34 for -1 yards (F.Whittaker).1-100NaNNaNNaN00CleanPuntNaNNaN0NaNNaN000NaN0NaNNaNNaN0NaNNaNNaNNaN0NaNI.BurseNaNF.WhittakerNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN026.010.016.016.0CARSD0NaN332320.0265020.3133020.0098100.4383630.0617000.0043360.1459870.00.0-2.8123840.987768NaNNaN0.8839680.1160320.9087260.0912740.8839680.024759NaNNaN2016
4520161211011831.031058.010.0SD34.066.010200.01.0SDCAR(2:38) P.Rivers pass deep middle to D.Inman to CAR 46 for 20 yards (D.Worley).12000NaNNaNNaN00NaNPassP.Rivers00-00229421CompleteDeep1820middle0NaNNaNNaN0NaNNaND.Inman00-00284111NaNNaNNaND.WorleyNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN010.026.0-16.016.0CARSD0NaN232320.0219320.1257370.0019720.1908190.2644710.0034790.3915900.00.01.8246171.4771721.3534720.1237000.9087260.0912740.8902950.1097050.0912740.0184310.0175700.0008612016
4620161211011831.031029.029.0CAR46.046.010220.00.0SDCAR(2:09) (No Huddle) K.Farrow up the middle to CAR 44 for 2 yards (V.Butler; D.Worley). SD-O.Franklin was injured during the play.1200NaNNaNNaN00NaNRunNaNNaN0NaNNaN000NaN0NaNC.Newton00-00329021middleNaNNaNNaN0NaNNaNNaNV.ButlerD.WorleyNaNNaN0NaNNaN00NaN0NaNNaNNaN010.026.0-16.016.0CARSD0NaN232320.0147890.0730260.0003420.1103780.3249740.0032330.4732580.00.03.301788-0.333565NaNNaN0.8902950.1097050.8960200.1039800.109705-0.005724NaNNaN2016
4720161211011832.021003.026.0CAR44.044.08440.01.0SDCAR(1:43) P.Rivers pass short left to Ty.Williams pushed ob at CAR 22 for 22 yards (L.Johnson).12200NaNNaNNaN00NaNPassP.Rivers00-00229421CompleteShort4180left0NaNNaNNaN0NaNNaNTy.Williams00-00321601NaNNaNNaNL.JohnsonNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN010.026.0-16.016.0CARSD0NaN232320.0168000.0801760.0005180.1221290.3451600.0034620.4317550.00.02.9682231.413426-0.2283191.6417450.8960200.1039800.8756080.1243920.1039800.020411-0.0033190.0237312016
4820161211011831.02978.025.0CAR22.022.010470.00.0SDCAR(1:18) K.Farrow right guard to CAR 19 for 3 yards (T.Davis). Kenny Wiggins reported as eligible1300NaNNaNNaN00NaNRunNaNNaN0NaNNaN000NaN0NaNJ.Stewart00-00329021lefttackleNaNNaN0NaNNaNNaNT.DavisNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN010.026.0-16.016.0CARSD0NaN232320.0087930.0339820.0000370.0510840.3720620.0026450.5313970.00.04.381650-0.236423NaNNaN0.8756080.1243920.8812180.1187820.124392-0.005610NaNNaN2016
4920161211011832.01938.040.0CAR19.019.07660.00.0SDCAR(:38) P.Rivers pass deep right to D.Inman for 19 yards, TOUCHDOWN.11911NaNNaNNaN00NaNPassP.Rivers00-00229421CompleteDeep1900right0NaNNaNNaN0NaNNaND.Inman00-00284111NaNNaNNaNNaNNaNNaNNaN0NaNNaN00NaN0NaNNaNNaN010.026.0-16.016.0CARSD0NaN232320.0101730.0345860.0000660.0526920.4215360.0026570.4782900.00.04.1452272.8547732.8547730.0000000.8812180.1187820.8248800.1751200.1187820.0563380.0563380.0000002016